Tag: Cloud

Building a Future-Proof Data Estate on Azure: Key Non-Functional Requirements for Success

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As organisations increasingly adopt data-driven strategies, managing and optimising large-scale data estates becomes a critical challenge. In modern data architectures, Azure’s suite of services offers powerful tools to manage complex data workflows, enabling businesses to unlock the value of their data efficiently and securely. One popular framework for organising and refining data is the Medallion Architecture, which provides a structured approach to managing data layers (bronze, silver, and gold) to ensure quality and accessibility.

When deploying an Azure data estate that utilises services such as Azure Data Lake Storage (ADLS) Gen2, Azure Synapse, Azure Data Factory, and Power BI, non-functional requirements (NFRs) play a vital role in determining the success of the project. While functional requirements describe what the system should do, NFRs focus on how the system should perform and behave under various conditions. They address key aspects such as performance, scalability, security, and availability, ensuring the solution is robust, reliable, and meets both technical and business needs.

In this post, we’ll explore the essential non-functional requirements for a data estate built on Azure, employing a Medallion Architecture. We’ll cover crucial areas such as data processing performance, security, availability, and maintainability—offering comprehensive insights to help you design and manage a scalable, high-performing Azure data estate that meets the needs of your business while keeping costs under control.

Let’s dive into the key non-functional aspects you should consider when planning and deploying your Azure data estate.

1. Performance

  • Data Processing Latency:
    • Define maximum acceptable latency for data movement through each stage of the Medallion Architecture (Bronze, Silver, Gold). For example, raw data ingested into ADLS-Gen2 (Bronze) should be processed into the Silver layer within 15 minutes and made available in the Gold layer within 30 minutes for analytics consumption.
    • Transformation steps in Azure Synapse should be optimised to ensure data is processed promptly for near real-time reporting in Power BI.
    • Specific performance KPIs could include batch processing completion times, such as 95% of all transformation jobs completing within the agreed SLA (e.g., 30 minutes).
  • Query Performance:
    • Define acceptable response times for typical and complex analytical queries executed against Azure Synapse. For instance, simple aggregation queries should return results within 2 seconds, while complex joins or analytical queries should return within 10 seconds.
    • Power BI visualisations pulling from Azure Synapse should render within 5 seconds for commonly used reports.
  • ETL Job Performance:
    • Azure Data Factory pipelines must complete ETL (Extract, Transform, Load) operations within a defined window. For example, daily data refresh pipelines should execute and complete within 2 hours, covering the full process of raw data ingestion, transformation, and loading into the Gold layer.
    • Batch processing jobs should run in parallel to enhance throughput without degrading the performance of other ongoing operations.
  • Concurrency and Throughput:
    • The solution must support a specified number of concurrent users and processes. For example, Azure Synapse should handle 100 concurrent query users without performance degradation.
    • Throughput requirements should define how much data can be ingested per unit of time (e.g., supporting the ingestion of 10 GB of data per hour into ADLS-Gen2).

2. Scalability

  • Data Volume Handling:
    • The system must scale horizontally and vertically to accommodate growing data volumes. For example, ADLS-Gen2 must support scaling from hundreds of gigabytes to petabytes of data as business needs evolve, without requiring significant rearchitecture of the solution.
    • Azure Synapse workloads should scale to handle increasing query loads from Power BI as more users access the data warehouse. Autoscaling should be triggered based on thresholds such as CPU usage, memory, and query execution times.
  • Compute and Storage Scalability:
    • Azure Synapse pools should scale elastically based on workload, with minimum and maximum numbers of Data Warehouse Units (DWUs) or vCores pre-configured for optimal cost and performance.
    • ADLS-Gen2 storage should scale to handle both structured and unstructured data with dynamic partitioning to ensure faster access times as data volumes grow.
  • ETL Scaling:
    • Azure Data Factory pipelines must support scaling by adding additional resources or parallelising processes as data volumes and the number of jobs increase. This ensures that data transformation jobs continue to meet their defined time windows, even as the workload increases.

3. Availability

  • Service Uptime:
    • A Service Level Agreement (SLA) should be defined for each Azure component, with ADLS-Gen2, Azure Synapse, and Power BI required to provide at least 99.9% uptime. This ensures that critical data services remain accessible to users and systems year-round.
    • Azure Data Factory pipelines should be resilient, capable of rerunning in case of transient failures without requiring manual intervention, ensuring data pipelines remain operational at all times.
  • Disaster Recovery (DR):
    • Define Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO) for critical Azure services. For example, ADLS-Gen2 should have an RPO of 15 minutes (data can be recovered up to the last 15 minutes before an outage), and an RTO of 2 hours (the system should be operational within 2 hours after an outage).
    • Azure Synapse and ADLS-Gen2 must replicate data across regions to support geo-redundancy, ensuring data availability in the event of regional outages.
  • Data Pipeline Continuity:
    • Azure Data Factory must support pipeline reruns, retries, and checkpoints to avoid data loss in the event of failure. Automated alerts should notify the operations team of any pipeline failures requiring human intervention.

4. Security

  • Data Encryption:
    • All data at rest in ADLS-Gen2, Azure Synapse, and in transit between services must be encrypted using industry standards (e.g., AES-256 for data at rest).
    • Transport Layer Security (TLS) should be enforced for data communication between services to ensure data in transit is protected from unauthorised access.
  • Role-Based Access Control (RBAC):
    • Access to all Azure resources (including ADLS-Gen2, Azure Synapse, and Azure Data Factory) should be restricted using RBAC. Specific roles (e.g., Data Engineers, Data Analysts) should be defined with corresponding permissions, ensuring that only authorised users can access or modify resources.
    • Privileged access should be minimised, with multi-factor authentication (MFA) required for high-privilege actions.
  • Data Masking:
    • Implement dynamic data masking in Azure Synapse or Power BI to ensure sensitive data (e.g., Personally Identifiable Information – PII) is masked or obfuscated for users without appropriate access levels, ensuring compliance with privacy regulations such as GDPR.
  • Network Security:
    • Ensure that all services are integrated using private endpoints and virtual networks (VNET) to restrict public internet exposure.
    • Azure Firewall or Network Security Groups (NSGs) should be used to protect data traffic between components within the architecture.

5. Maintainability

  • Modular Pipelines:
    • Azure Data Factory pipelines should be built in a modular fashion, allowing individual pipeline components to be reused across different workflows. This reduces maintenance overhead and allows for quick updates.
    • Pipelines should be version-controlled using Azure DevOps or Git, with CI/CD pipelines established for deployment automation.
  • Documentation and Best Practices:
    • All pipelines, datasets, and transformations should be documented to ensure new team members can easily understand and maintain workflows.
    • Adherence to best practices, including naming conventions, tagging, and modular design, should be mandatory.
  • Monitoring and Logging:
    • Azure Monitor and Azure Log Analytics must be used to log and monitor the health of pipelines, resource usage, and performance metrics across the architecture.
    • Proactive alerts should be configured to notify of pipeline failures, data ingestion issues, or performance degradation.

6. Compliance

  • Data Governance:
    • Azure Purview (or a similar governance tool) should be used to catalogue all datasets in ADLS-Gen2 and Azure Synapse. This ensures that the organisation has visibility into data lineage, ownership, and classification across the data estate.
    • Data lifecycle management policies should be established to automatically delete or archive data after a certain period (e.g., archiving data older than 5 years).
  • Data Retention and Archiving:
    • Define clear data retention policies for data stored in ADLS-Gen2. For example, operational data in the Bronze layer should be archived after 6 months, while Gold data might be retained for longer periods.
    • Archiving should comply with regulatory requirements, and archived data must still be recoverable within a specified period (e.g., within 24 hours).
  • Auditability:
    • All access and actions performed on data in ADLS-Gen2, Azure Synapse, and Azure Data Factory should be logged for audit purposes. Audit logs must be retained for a defined period (e.g., 7 years) and made available for compliance reporting when required.

7. Reliability

  • Data Integrity:
    • Data validation and reconciliation processes should be implemented at each stage (Bronze, Silver, Gold) to ensure that data integrity is maintained throughout the pipeline. Any inconsistencies should trigger alerts and automated corrective actions.
    • Schema validation must be enforced to ensure that changes in source systems do not corrupt data as it flows through the layers.
  • Backup and Restore:
    • Periodic backups of critical data in ADLS-Gen2 and Azure Synapse should be scheduled to ensure data recoverability in case of corruption or accidental deletion.
    • Test restore operations should be performed quarterly to ensure backups are valid and can be restored within the RTO.

8. Cost Optimisation

  • Resource Usage Efficiency:
    • Azure services must be configured to use cost-effective resources, with cost management policies in place to avoid unnecessary expenses. For example, Azure Synapse compute resources should be paused during off-peak hours to minimise costs.
    • Data lifecycle policies in ADLS-Gen2 should archive older, infrequently accessed data to lower-cost storage tiers (e.g., cool or archive).
  • Cost Monitoring:
    • Set up cost alerts using Azure Cost Management to monitor usage and avoid unexpected overspends. Regular cost reviews should be conducted to identify areas of potential savings.

9. Interoperability

  • External System Integration:
    • The system must support integration with external systems such as third-party APIs or on-premise databases, with Azure Data Factory handling connectivity and orchestration.
    • Data exchange formats such as JSON, Parquet, or CSV should be supported to ensure compatibility across various platforms and services.

10. Licensing

When building a data estate on Azure using services such as Azure Data Lake Storage (ADLS) Gen2, Azure Synapse, Azure Data Factory, and Power BI, it’s essential to understand the licensing models and associated costs for each service. Azure’s licensing follows a pay-as-you-go model, offering flexibility, but it requires careful management to avoid unexpected costs. Below are some key licensing considerations for each component:

  • Azure Data Lake Storage (ADLS) Gen2:
    • Storage Costs: ADLS Gen2 charges are based on the volume of data stored and the access tier selected (hot, cool, or archive). The hot tier, offering low-latency access, is more expensive, while the cool and archive tiers are more cost-effective but designed for infrequently accessed data.
    • Data Transactions: Additional charges apply for data read and write transactions, particularly if the data is accessed frequently.
  • Azure Synapse:
    • Provisioned vs On-Demand Pricing: Azure Synapse offers two pricing models. The provisioned model charges based on the compute resources allocated (Data Warehouse Units or DWUs), which are billed regardless of actual usage. The on-demand model charges per query, offering flexibility for ad-hoc analytics workloads.
    • Storage Costs: Data stored in Azure Synapse also incurs storage costs, based on the size of the datasets within the service.
  • Azure Data Factory (ADF):
    • Pipeline Runs: Azure Data Factory charges are based on the number of pipeline activities executed. Each data movement or transformation activity incurs costs based on the volume of data processed and the frequency of pipeline executions.
    • Integration Runtime: Depending on the region or if on-premises data is involved, using the integration runtime can incur additional costs, particularly for large data transfers across regions or in hybrid environments.
  • Power BI:
    • Power BI Licensing: Power BI offers Free, Pro, and Premium licensing tiers. The Free tier is suitable for individual users with limited sharing capabilities, while Power BI Pro offers collaboration features at a per-user cost. Power BI Premium provides enhanced performance, dedicated compute resources, and additional enterprise-grade features, which are priced based on capacity rather than per user.
    • Data Refreshes: The number of dataset refreshes per day is limited in the Power BI Pro tier, while the Premium tier allows for more frequent and larger dataset refreshes.

Licensing plays a crucial role in the cost and compliance management of a Dev, Test, and Production environment involving services like Azure Data Lake Storage Gen2 (ADLS Gen2), Azure Data Factory (ADF), Synapse Analytics, and Power BI. Each of these services has specific licensing considerations, especially as usage scales across environments.

10.1 Development Environment

  • Azure Data Lake Storage Gen2 (ADLS Gen2): The development environment typically incurs minimal licensing costs as storage is charged based on the amount of data stored, operations performed, and redundancy settings. Usage should be low, and developers can manage costs by limiting data ingestion and using lower redundancy options.
  • Azure Data Factory (ADF): ADF operates on a consumption-based model where costs are based on the number of pipeline runs and data movement activities. For development, licensing costs are minimal, but care should be taken to avoid unnecessary pipeline executions and data transfers.
  • Synapse Analytics: For development, developers may opt for the pay-as-you-go pricing model with minimal resources. Synapse offers a “Development” SKU for non-production environments, which can reduce costs. Dedicated SQL pools should be minimized in Dev to reduce licensing costs, and serverless options should be considered.
  • Power BI: Power BI Pro licenses are usually required for developers to create and share reports. A lower number of licenses can be allocated for development purposes, but if collaboration and sharing are involved, a Pro license will be necessary. If embedding Power BI reports, Power BI Embedded SKU licensing should also be considered.

10.2 Test Environment

  • Azure Data Lake Storage Gen2 (ADLS Gen2): Licensing in the test environment should mirror production but at a smaller scale. Costs will be related to storage and I/O operations, similar to the production environment, but with the potential for cost savings through lower data volumes or reduced redundancy settings.
  • Azure Data Factory (ADF): Testing activities typically generate higher consumption than development due to load testing, integration testing, and data movement simulations. Usage-based licensing for data pipelines and data flows will apply. It is important to monitor the cost of ADF runs and ensure testing does not consume excessive resources unnecessarily.
  • Synapse Analytics: For the test environment, the pricing model should mirror production usage with the possibility of scaling down in terms of computing power. Testing should focus on Synapse’s workload management to ensure performance in production while minimizing licensing costs. Synapse’s “Development” or lower-tier options could still be leveraged to reduce costs during non-critical testing periods.
  • Power BI: Power BI Pro licenses are typically required for testing reports and dashboards. Depending on the scope of testing, you may need a few additional licenses, but overall testing should not significantly increase licensing costs. If Power BI Premium or Embedded is being used in production, it may be necessary to have similar licensing in the test environment for accurate performance and load testing.

10.3 Production Environment

  • Azure Data Lake Storage Gen2 (ADLS Gen2): Licensing is based on the volume of data stored, redundancy options (e.g., LRS, GRS), and operations performed (e.g., read/write transactions). In production, it is critical to consider data lifecycle management policies, such as archiving and deletion, to optimize costs while staying within licensing agreements.
  • Azure Data Factory (ADF): Production workloads in ADF are licensed based on consumption, specifically pipeline activities, data integration operations, and Data Flow execution. It’s important to optimize pipeline design to reduce unnecessary executions or long-running activities. ADF also offers Managed VNET pricing for enhanced security, which might affect licensing costs.
  • Synapse Analytics: For Synapse Analytics, production environments can leverage either the pay-as-you-go pricing model for serverless SQL pools or reserved capacity (for dedicated SQL pools) to lock in lower pricing over time. The licensing cost in production can be significant if heavy data analytics workloads are running, so careful monitoring and workload optimization are necessary.
  • Power BI: For production reporting, Power BI offers two main licensing options:
    • Power BI Pro: This license is typically used for individual users, and each user who shares or collaborates on reports will need a Pro license.
    • Power BI Premium: Premium provides dedicated cloud compute and storage for larger enterprise users, offering scalability and performance enhancements. Licensing is either capacity-based (Premium Per Capacity) or user-based (Premium Per User). Power BI Premium is especially useful for large-scale, enterprise-wide reporting solutions.
    • Depending on the nature of production use (whether reports are shared publicly or embedded), Power BI Embedded licenses may also be required for embedded analytics in custom applications. This is typically licensed based on compute capacity (e.g., A1-A6 SKUs).

License Optimization Across Environments

  • Cost Control with Reserved Instances: For production, consider reserved capacity for Synapse Analytics and other Azure services to lock in lower pricing over 1- or 3-year periods. This is particularly beneficial when workloads are predictable.
  • Developer and Test Licensing Discounts: Azure often offers discounted pricing for Dev/Test environments. Azure Dev/Test pricing is available for active Visual Studio subscribers, providing significant savings for development and testing workloads. This can reduce the cost of running services like ADF, Synapse, and ADLS Gen2 in non-production environments.
  • Power BI Embedded vs Premium: If Power BI is being embedded in a web or mobile application, you can choose between Power BI Embedded (compute-based pricing) or Power BI Premium (user-based pricing) depending on whether you need to share reports externally or internally. Evaluate which model works best for cost optimization based on your report sharing patterns.

11. User Experience (Power BI)

  • Dashboard Responsiveness:
    • Power BI dashboards querying data from Azure Synapse should render visualisations within a specified time (e.g., less than 5 seconds for standard reports) to ensure a seamless user experience.
    • Power BI reports should be optimised to ensure quick refreshes and minimise unnecessary queries to the underlying data warehouse.
  • Data Refresh Frequenc
    • Define how frequently Power BI reports must refresh based on the needs of the business. For example, data should be updated every 15 minutes for dashboards that track near real-time performance metrics.

12. Environment Management: Development, Testing (UAT), and Production

Managing different environments is crucial to ensure that changes to your Azure data estate are deployed systematically, reducing risks, ensuring quality, and maintaining operational continuity. It is essential to have distinct environments for Development, Testing/User Acceptance Testing (UAT), and Production. Each environment serves a specific purpose and helps ensure the overall success of the solution. Here’s how you should structure and manage these environments:

12.1 Development Environment

  • Purpose:
    The Development environment is where new features, enhancements, and fixes are first developed. This environment allows developers and data engineers to build and test individual components such as data pipelines, models, and transformations without impacting live data or users.
  • Characteristics:
    • Resources should be provisioned based on the specific requirements of the development team, but they can be scaled down to reduce costs.
    • Data used in development should be synthetic or anonymised to prevent any exposure of sensitive information.
    • CI/CD Pipelines: Set up Continuous Integration (CI) pipelines to automate the testing and validation of new code before it is promoted to the next environment.
  • Security and Access:
    • Developers should have the necessary permissions to modify resources, but strong access controls should still be enforced to avoid accidental changes or misuse.
    • Multi-factor authentication (MFA) should be enabled for access.

12.2 Testing and User Acceptance Testing (UAT) Environment

  • Purpose:
    The Testing/UAT environment is used to validate new features and bug fixes in a production-like setting. This environment mimics the Production environment to catch any issues before deployment to live users. Testing here ensures that the solution meets business and technical requirements.
  • Characteristics:
    • Data: The data in this environment should closely resemble the production data, but should ideally be anonymised or masked to protect sensitive information.
    • Performance Testing: Conduct performance testing in this environment to ensure that the system can handle the expected load in production, including data ingestion rates, query performance, and concurrency.
    • Functional Testing: Test new ETL jobs, data transformations, and Power BI reports to ensure they behave as expected.
    • UAT: Business users should be involved in testing to ensure that new features meet their requirements and that the system behaves as expected from an end-user perspective.
  • Security and Access:
    • Developers, testers, and business users involved in UAT should have appropriate levels of access, but sensitive data should still be protected through masking or anonymisation techniques.
    • User roles in UAT should mirror production roles to ensure testing reflects real-world access patterns.
  • Automated Testing:
    • Automate tests for pipelines and queries where possible to validate data quality, performance, and system stability before moving changes to Production.

12.3 Production Environment

  • Purpose:
    The Production environment is the live environment that handles real data and user interactions. It is mission-critical, and ensuring high availability, security, and performance in this environment is paramount.
  • Characteristics:
    • Service Uptime: The production environment must meet strict availability SLAs, typically 99.9% uptime for core services such as ADLS-Gen2, Azure Synapse, Azure Data Factory, and Power BI.
    • High Availability and Disaster Recovery: Production environments must have disaster recovery mechanisms, including data replication across regions and failover capabilities, to ensure business continuity in the event of an outage.
    • Monitoring and Alerts: Set up comprehensive monitoring using Azure Monitor and other tools to track performance metrics, system health, and pipeline executions. Alerts should be configured for failures, performance degradation, and cost anomalies.
  • Change Control:
    • Any changes to the production environment must go through formal Change Management processes. This includes code reviews, approvals, and staged deployments (from Development > Testing > Production) to minimise risk.
    • Use Azure DevOps or another CI/CD tool to automate deployments to production. Rollbacks should be available to revert to a previous stable state if issues arise.
  • Security and Access:
    • Strict access controls are essential in production. Only authorised personnel should have access to the environment, and all changes should be tracked and logged.
    • Data Encryption: Ensure that data in production is encrypted at rest and in transit using industry-standard encryption protocols.

12.4 Data Promotion Across Environments

  • Data Movement:
    • When promoting data pipelines, models, or new code across environments, automated testing and validation must ensure that all changes function correctly in each environment before reaching Production.
    • Data should only be moved from Development to UAT and then to Production through secure pipelines. Use Azure Data Factory or Azure DevOps for data promotion and automation.
  • Versioning:
    • Maintain version control across all environments. Any changes to pipelines, models, and queries should be tracked and revertible, ensuring stability and security as new features are tested and deployed.

13. Workspaces and Sandboxes in the Development Environment

In addition to the non-functional requirements, effective workspaces and sandboxes are essential for development in Azure-based environments. These structures provide isolated and flexible environments where developers can build, test, and experiment without impacting production workloads.

Workspaces and Sandboxes Overview

  • Workspaces: A workspace is a logical container where developers can collaborate and organise their resources, such as data, pipelines, and code. Azure Synapse Analytics, Power BI, and Azure Machine Learning use workspaces to manage resources and workflows efficiently.
  • Sandboxes: Sandboxes are isolated environments that allow developers to experiment and test their configurations, code, or infrastructure without interfering with other developers or production environments. Sandboxes are typically temporary and can be spun up or destroyed as needed, often implemented using infrastructure-as-code (IaC) tools.

Non-Functional Requirements for Workspaces and Sandboxes in the Dev Environment

13.1 Isolation and Security

  • Workspace Isolation: Developers should be able to create independent workspaces in Synapse Analytics and Power BI to develop pipelines, datasets, and reports without impacting production data or resources. Each workspace should have its own permissions and access controls.
  • Sandbox Isolation: Each developer or development team should have access to isolated sandboxes within the Dev environment. This prevents interference from others working on different projects and ensures that errors or experimental changes do not affect shared resources.
  • Role-Based Access Control (RBAC): Enforce RBAC in both workspaces and sandboxes. Developers should have sufficient privileges to build and test solutions but should not have access to sensitive production data or environments.

13.2 Scalability and Flexibility

  • Elastic Sandboxes: Sandboxes should allow developers to scale compute resources up or down based on the workload (e.g., Synapse SQL pools, ADF compute clusters). This allows efficient testing of both lightweight and complex data scenarios.
  • Customisable Workspaces: Developers should be able to customise workspace settings, such as data connections and compute options. In Power BI, this means configuring datasets, models, and reports, while in Synapse, it involves managing linked services, pipelines, and other resources.

13.3 Version Control and Collaboration

  • Source Control Integration: Workspaces and sandboxes should integrate with source control systems like GitHub or Azure Repos, enabling developers to collaborate on code and ensure versioning and tracking of all changes (e.g., Synapse SQL scripts, ADF pipelines).
  • Collaboration Features: Power BI workspaces, for example, should allow teams to collaborate on reports and dashboards. Shared development workspaces should enable team members to co-develop, review, and test Power BI reports while maintaining control over shared resources.

13.4 Automation and Infrastructure-as-Code (IaC)

  • Automated Provisioning: Sandboxes and workspaces should be provisioned using IaC tools like Azure Resource Manager (ARM) templates, Terraform, or Bicep. This allows for quick setup, teardown, and replication of environments as needed.
  • Automated Testing in Sandboxes: Implement automated testing within sandboxes to validate changes in data pipelines, transformations, and reporting logic before promoting to the Test or Production environments. This ensures data integrity and performance without manual intervention.

13.5 Cost Efficiency

  • Ephemeral Sandboxes: Design sandboxes as ephemeral environments that can be created and destroyed as needed, helping control costs by preventing resources from running when not in use.
  • Workspace Optimisation: Developers should use lower-cost options in workspaces (e.g., smaller compute nodes in Synapse, reduced-scale datasets in Power BI) to limit resource consumption. Implement cost-tracking tools to monitor and optimise resource usage.

13.6 Data Masking and Sample Data

  • Data Masking: Real production data should not be used in the Dev environment unless necessary. Data masking or anonymisation should be implemented within workspaces and sandboxes to ensure compliance with data protection policies.
  • Sample Data: Developers should work with synthetic or representative sample data in sandboxes to simulate real-world scenarios. This minimises the risk of exposing sensitive production data while enabling meaningful testing.

13.7 Cross-Service Integration

  • Synapse Workspaces: Developers in Synapse Analytics should easily integrate resources like Azure Data Factory pipelines, ADLS Gen2 storage accounts, and Synapse SQL pools within their workspaces, allowing development and testing of end-to-end data pipelines.
  • Power BI Workspaces: Power BI workspaces should be used for developing and sharing reports and dashboards during development. These workspaces should be isolated from production and tied to Dev datasets.
  • Sandbox Connectivity: Sandboxes in Azure should be able to access shared development resources (e.g., ADLS Gen2) to test integration flows (e.g., ADF data pipelines and Synapse integration) without impacting other projects.

13.8 Lifecycle Management

  • Resource Lifecycle: Sandbox environments should have predefined expiration times or automated cleanup policies to ensure resources are not left running indefinitely, helping manage cloud sprawl and control costs.
  • Promotion to Test/Production: Workspaces and sandboxes should support workflows where development work can be moved seamlessly to the Test environment (via CI/CD pipelines) and then to Production, maintaining a consistent process for code and data pipeline promotion.

Key Considerations for Workspaces and Sandboxes in the Dev Environment

  • Workspaces in Synapse Analytics and Power BI are critical for organising resources like pipelines, datasets, models, and reports.
  • Sandboxes provide safe, isolated environments where developers can experiment and test changes without impacting shared resources or production systems.
  • Automation and Cost Efficiency are essential. Ephemeral sandboxes, Infrastructure-as-Code (IaC), and automated testing help reduce costs and ensure agility in development.
  • Data Security and Governance must be maintained even in the development stage, with data masking, access controls, and audit logging applied to sandboxes and workspaces.

By incorporating these additional structures and processes for workspaces and sandboxes, organisations can ensure their development environments are flexible, secure, and cost-effective. This not only accelerates development cycles but also ensures quality and compliance across all phases of development.

These detailed non-functional requirements provide a clear framework to ensure that the data estate is performant, secure, scalable, and cost-effective, while also addressing compliance and user experience concerns.

Conclusion

Designing and managing a data estate on Azure, particularly using a Medallion Architecture, involves much more than simply setting up data pipelines and services. The success of such a solution depends on ensuring that non-functional requirements (NFRs), such as performance, scalability, security, availability, and maintainability, are carefully considered and rigorously implemented. By focusing on these critical aspects, organisations can build a data architecture that is not only efficient and reliable but also capable of scaling with the growing demands of the business.

Azure’s robust services, such as ADLS Gen2, Azure Synapse, Azure Data Factory, and Power BI, provide a powerful foundation, but without the right NFRs in place, even the most advanced systems can fail to meet business expectations. Ensuring that data flows seamlessly through the bronze, silver, and gold layers, while maintaining high performance, security, and cost efficiency, will enable organisations to extract maximum value from their data.

Incorporating a clear strategy for each non-functional requirement will help you future-proof your data estate, providing a solid platform for innovation, improved decision-making, and business growth. By prioritising NFRs, you can ensure that your Azure data estate is more than just operational—it becomes a competitive asset for your organisation.

Embracing Modern Cloud-Based Application Architecture with Microsoft Azure

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In cloud computing, Microsoft Azure offers a robust framework for building modern cloud-based applications. Designed to enhance scalability, flexibility, and resilience, Azure’s comprehensive suite of services empowers developers to create efficient and robust solutions. Let’s dive into the core components of this architecture in detail.

1. Microservices Architecture

Overview:
Microservices architecture breaks down applications into small, independent services, each performing a specific function. These services communicate over well-defined APIs, enabling a modular approach to development.

Advantages:

  • Modularity: Easier to develop, test, and deploy individual components.
  • Scalability: Services can be scaled independently based on demand.
  • Deployability: Faster deployment cycles since services can be updated independently without affecting the whole system.
  • Fault Isolation: Failures in one service do not impact the entire system.

Key Azure Services:

  • Azure Kubernetes Service (AKS): Provides a managed Kubernetes environment for deploying, scaling, and managing containerised applications.
  • Azure Service Fabric: A distributed systems platform for packaging, deploying, and managing scalable and reliable microservices.

2. Containers and Orchestration

Containers:
Containers encapsulate an application and its dependencies, ensuring consistency across multiple environments. They provide a lightweight, portable, and efficient alternative to virtual machines.

Orchestration:
Orchestration tools manage the deployment, scaling, and operation of containers, ensuring that containerised applications run smoothly across different environments.

Advantages:

  • Consistency: Ensures that applications run the same way in development, testing, and production.
  • Efficiency: Containers use fewer resources compared to virtual machines.
  • Portability: Easily move applications between different environments or cloud providers.

Key Azure Services:

  • Azure Kubernetes Service (AKS): Manages Kubernetes clusters, automating tasks such as scaling, updates, and provisioning.
  • Azure Container Instances: Provides a quick and easy way to run containers without managing the underlying infrastructure.

3. Serverless Computing

Overview:
Serverless computing allows developers to run code in response to events without managing servers. The cloud provider automatically provisions, scales, and manages the infrastructure required to run the code.

Advantages:

  • Simplified Deployment: Focus on code rather than infrastructure management.
  • Cost Efficiency: Pay only for the compute time used when the code is running.
  • Automatic Scaling: Automatically scales based on the load and usage patterns.

Key Azure Services:

  • Azure Functions: Enables you to run small pieces of code (functions) without provisioning or managing servers.
  • Azure Logic Apps: Facilitates the automation of workflows and integration with various services and applications.

4. APIs and API Management

APIs:
APIs (Application Programming Interfaces) enable communication between different services and components, acting as a bridge that allows them to interact.

API Management:
API Management involves securing, monitoring, and managing API traffic. It provides features like rate limiting, analytics, and a single entry point for accessing APIs.

Advantages:

  • Security: Protects APIs from misuse and abuse.
  • Management: Simplifies the management and monitoring of API usage.
  • Scalability: Supports scaling by managing API traffic effectively.

Key Azure Services:

  • Azure API Management: A comprehensive solution for managing APIs, providing security, analytics, and monitoring capabilities.

5. Event-Driven Architecture

Overview:
Event-driven architecture uses events to trigger actions and facilitate communication between services. This approach decouples services, allowing them to operate independently and respond to real-time changes.

Advantages:

  • Decoupling: Services can operate independently, reducing dependencies.
  • Responsiveness: Real-time processing of events improves the responsiveness of applications.
  • Scalability: Easily scale services based on event load.

Key Azure Services:

  • Azure Event Grid: Simplifies the creation and management of event-based architectures by routing events from various sources to event handlers.
  • Azure Service Bus: A reliable message broker that enables asynchronous communication between services.
  • Azure Event Hubs: A big data streaming platform for processing and analysing large volumes of events.

6. Databases and Storage

Relational Databases:
Relational databases, like Azure SQL Database, are ideal for structured data and support ACID (Atomicity, Consistency, Isolation, Durability) properties.

NoSQL Databases:
NoSQL databases, such as Azure Cosmos DB, handle unstructured or semi-structured data, offering flexibility, scalability, and performance.

Object Storage:
Object storage solutions like Azure Blob Storage are used for storing large amounts of unstructured data, such as media files and backups.

Advantages:

  • Flexibility: Choose the right database based on the data type and application requirements.
  • Scalability: Scale databases and storage solutions to handle varying loads.
  • Performance: Optimise performance based on the workload characteristics.

Key Azure Services:

  • Azure SQL Database: A fully managed relational database service with built-in intelligence.
  • Azure Cosmos DB: A globally distributed, multi-model database service for any scale.
  • Azure Blob Storage: A scalable object storage service for unstructured data.

7. Load Balancing and Traffic Management

Overview:
Load balancing distributes incoming traffic across multiple servers or services to ensure reliability and performance. Traffic management involves routing traffic based on various factors like geographic location or server health.

Advantages:

  • Availability: Ensures that services remain available even if some instances fail.
  • Performance: Distributes load evenly to prevent any single server from becoming a bottleneck.
  • Scalability: Easily add or remove instances based on traffic demands.

Key Azure Services:

  • Azure Load Balancer: Distributes network traffic across multiple servers to ensure high availability and reliability.
  • Azure Application Gateway: A web traffic load balancer that provides advanced routing capabilities, including SSL termination and session affinity.

8. Monitoring and Logging

Monitoring:
Monitoring tracks the performance and health of applications and infrastructure, providing insights into their operational state.

Logging:
Logging involves collecting and analysing log data for troubleshooting, performance optimisation, and security auditing.

Advantages:

  • Visibility: Gain insights into application performance and infrastructure health.
  • Troubleshooting: Quickly identify and resolve issues based on log data.
  • Optimisation: Use monitoring data to optimise performance and resource usage.

Key Azure Services:

  • Azure Monitor: Provides comprehensive monitoring of applications and infrastructure, including metrics, logs, and alerts.
  • Azure Log Analytics: Collects and analyses log data from various sources, enabling advanced queries and insights.

9. Security

IAM (Identity and Access Management):
IAM manages user identities and access permissions to resources, ensuring that only authorised users can access sensitive data and applications.

Encryption:
Encryption protects data in transit and at rest, ensuring that it cannot be accessed or tampered with by unauthorised parties.

WAF (Web Application Firewall):
A WAF protects web applications from common threats and vulnerabilities, such as SQL injection and cross-site scripting (XSS).

Advantages:

  • Access Control: Manage user permissions and access to resources effectively.
  • Data Protection: Secure sensitive data with encryption and other security measures.
  • Threat Mitigation: Protect applications from common web exploits.

Key Azure Services:

  • Azure Active Directory: A comprehensive identity and access management service.
  • Azure Key Vault: Securely stores and manages sensitive information, such as encryption keys and secrets.
  • Azure Security Centre: Provides unified security management and advanced threat protection.
  • Azure Web Application Firewall: Protects web applications from common threats and vulnerabilities.

10. CI/CD Pipelines

Overview:
CI/CD (Continuous Integration/Continuous Deployment) pipelines automate the processes of building, testing, and deploying applications, ensuring that new features and updates are delivered quickly and reliably.

Advantages:

  • Efficiency: Automate repetitive tasks, reducing manual effort and errors.
  • Speed: Accelerate the deployment of new features and updates.
  • Reliability: Ensure that code changes are thoroughly tested before deployment.

Key Azure Services:

  • Azure DevOps: Provides a suite of tools for managing the entire application lifecycle, including CI/CD pipelines.
  • GitHub Actions: Automates workflows directly within GitHub, including CI/CD pipelines.

11. Configuration Management

Overview:
Configuration management involves managing the configuration and state of applications across different environments, ensuring consistency and automating infrastructure management tasks.

Advantages:

  • Consistency: Ensure that applications and infrastructure are configured consistently across environments.
  • Automation: Automate the deployment and management of infrastructure.
  • Version Control: Track and manage changes to configurations over time.

Key Azure Services:

  • Azure Resource Manager: Provides a consistent management layer for deploying and managing Azure resources.
  • Azure Automation: Automates repetitive tasks and orchestrates complex workflows.
  • Terraform on Azure: An open-source tool for building, changing, and versioning infrastructure safely and efficiently.

12. Edge Computing and CDN

Edge Computing:
Edge computing processes data closer to the source (e.g., IoT devices) to reduce latency and improve responsiveness.

CDN (Content Delivery Network):
A CDN distributes content globally, reducing latency and improving load times for users by caching content at strategically located edge nodes.

Advantages:

  • Latency Reduction: Process data closer to the source to minimise delays.
  • Performance Improvement: Deliver content faster by caching it closer to users.
  • Scalability: Handle large volumes of traffic efficiently.

Key Azure Services:

  • Azure IoT Edge: Extends cloud intelligence to edge devices, enabling data processing and analysis closer to the data source.
  • Azure Content Delivery Network (CDN): Delivers high-bandwidth content to users globally by caching content at edge locations.

Example Architecture on Azure

Frontend:

  • Hosting: Deploy the frontend on Azure CDN for fast, global delivery (e.g., React app).
  • API Communication: Communicate with backend services via APIs.

Backend:

  • Microservices: Deploy microservices in containers managed by Azure Kubernetes Service (AKS).
  • Serverless Functions: Use Azure Functions for specific tasks that require quick execution.

Data Layer:

  • Databases: Combine relational databases (e.g., Azure SQL Database) and NoSQL databases (e.g., Azure Cosmos DB) for different data needs.
  • Storage: Use Azure Blob Storage for storing media files and large datasets.

Communication:

  • Event-Driven: Implement event-driven architecture with Azure Event Grid for inter-service communication.
  • API Management: Manage and secure API requests using Azure API Management.

Security:

  • Access Control: Use Azure Active Directory for managing user identities and access permissions.
  • Threat Protection: Protect applications with Azure Web Application Firewall.

DevOps:

  • CI/CD: Set up CI/CD pipelines with Azure DevOps for automated testing and deployment.
  • Monitoring and Logging: Monitor applications with Azure Monitor and analyse logs with Azure Log Analytics.

Conclusion

Leveraging Microsoft Azure for modern cloud-based application architecture provides a robust and scalable foundation for today’s dynamic business environments. By integrating these key components, businesses can achieve high availability, resilience, and the flexibility to adapt rapidly to changing demands while maintaining robust security and operational efficiency.

Cloud Computing: Strategies for Scalability and Flexibility

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Day 3 of Renier Botha’s 10-Day Blog Series on Navigating the Future: The Evolving Role of the CTO

Cloud computing has transformed the way businesses operate, offering unparalleled scalability, flexibility, and cost savings. However, as organizations increasingly rely on cloud technologies, they also face unique challenges. This blog post explores hybrid and multi-cloud strategies that CTOs can adopt to maximize the benefits of cloud computing while navigating its complexities. We will also include insights from industry leaders and real-world examples to illustrate these concepts.

The Benefits of Cloud Computing

Cloud computing allows businesses to access and manage data and applications over the internet, eliminating the need for on-premises infrastructure. The key benefits include:

  • Scalability: Easily scale resources up or down based on demand, ensuring optimal performance without overprovisioning.
  • Flexibility: Access applications and data from anywhere, supporting remote work and collaboration.
  • Cost Savings: Pay-as-you-go pricing models reduce capital expenditures on hardware and software.
  • Resilience: Ensure continuous operation and rapid recovery from disruptions by leveraging robust, redundant cloud infrastructure and advanced failover mechanisms.
  • Disaster Recovery: Cloud services offer robust backup and disaster recovery solutions.
  • Innovation: Accelerate the deployment of new applications and services, fostering innovation and competitive advantage.

Challenges of Cloud Computing

Despite these advantages, cloud computing presents several challenges:

  • Security and Compliance: Ensuring data security and regulatory compliance in the cloud.
  • Cost Management: Controlling and optimizing cloud costs.
  • Vendor Lock-In: Avoiding dependency on a single cloud provider.
  • Performance Issues: Managing latency and ensuring consistent performance.

Hybrid and Multi-Cloud Strategies

To address these challenges and harness the full potential of cloud computing, many organizations are adopting hybrid and multi-cloud strategies.

Hybrid Cloud Strategy

A hybrid cloud strategy combines on-premises infrastructure with public and private cloud services. This approach offers greater flexibility and control, allowing businesses to:

  • Maintain Control Over Critical Data: Keep sensitive data on-premises while leveraging the cloud for less critical workloads.
  • Optimize Workloads: Run workloads where they perform best, whether on-premises or in the cloud.
  • Improve Disaster Recovery: Use cloud resources for backup and disaster recovery while maintaining primary operations on-premises.

Quote: “Hybrid cloud is about having the freedom to choose the best location for your workloads, balancing the need for control with the benefits of cloud agility.” – Arvind Krishna, CEO of IBM

Multi-Cloud Strategy

A multi-cloud strategy involves using multiple cloud services from different providers. This approach helps organizations avoid vendor lock-in, optimize costs, and enhance resilience. Benefits include:

  • Avoiding Vendor Lock-In: Flexibility to switch providers based on performance, cost, and features.
  • Cost Optimization: Choose the most cost-effective services for different workloads.
  • Enhanced Resilience: Distribute workloads across multiple providers to improve availability and disaster recovery.

Quote: “The future of cloud is multi-cloud. Organizations are looking for flexibility and the ability to innovate without being constrained by a single vendor.” – Thomas Kurian, CEO of Google Cloud

Real-World Examples

Example 1: Netflix

Netflix is a prime example of a company leveraging a multi-cloud strategy. While AWS is its primary cloud provider, Netflix also uses Google Cloud and Azure to enhance resilience and avoid downtime. By distributing its workloads across multiple clouds, Netflix ensures high availability and performance for its global user base.

Example 2: General Electric (GE)

GE employs a hybrid cloud strategy to optimize its industrial operations. By keeping critical data on-premises and using the cloud for analytics and IoT applications, GE balances control and agility. This approach has enabled GE to improve predictive maintenance, reduce downtime, and enhance operational efficiency.

Example 3: Capital One

Capital One uses a hybrid cloud strategy to meet regulatory requirements while benefiting from cloud scalability. Sensitive financial data is stored on-premises, while less sensitive workloads are run in the cloud. This strategy allows Capital One to innovate rapidly while ensuring data security and compliance.

Implementing Hybrid and Multi-Cloud Strategies

To successfully implement hybrid and multi-cloud strategies, CTOs should consider the following steps:

  1. Assess Workloads: Identify which workloads are best suited for on-premises, public cloud, or private cloud environments.
  2. Select Cloud Providers: Choose cloud providers based on their strengths, cost, and compatibility with your existing infrastructure.
  3. Implement Cloud Management Tools: Use cloud management platforms to monitor and optimize multi-cloud environments.
  4. Ensure Security and Compliance: Implement robust security measures and ensure compliance with industry regulations.
  5. Train Staff: Provide training for IT staff to manage and optimize hybrid and multi-cloud environments effectively.

The Three Major Cloud Providers: Microsoft Azure, AWS, and GCP

When selecting cloud providers, many organizations consider the three major players in the market: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). Each of these providers offers unique strengths and capabilities.

Microsoft Azure

Microsoft Azure is known for its seamless integration with Microsoft’s software ecosystem, making it a popular choice for businesses already using Windows Server, SQL Server, and other Microsoft products.

  • Strengths: Strong enterprise integration, extensive hybrid cloud capabilities, comprehensive AI and ML tools.
  • Use Case: Johnson Controls uses Azure for its OpenBlue platform, integrating IoT and AI to enhance building management and energy efficiency.

Quote: “Microsoft Azure is a trusted cloud platform for enterprises, enabling seamless integration with existing Microsoft tools and services.” – Satya Nadella, CEO of Microsoft

Amazon Web Services (AWS)

AWS is the largest and most widely adopted cloud platform, known for its extensive range of services, scalability, and reliability. It offers a robust infrastructure and a vast ecosystem of third-party integrations.

  • Strengths: Wide range of services, scalability, strong developer tools, global presence.
  • Use Case: Airbnb uses AWS to handle its massive scale of operations, leveraging AWS’s compute and storage services to manage millions of bookings and users.

Quote: “AWS enables businesses to scale and innovate faster, providing the most comprehensive and broadly adopted cloud platform.” – Andy Jassy, CEO of Amazon

Google Cloud Platform (GCP)

GCP is recognized for its strong capabilities in data analytics, machine learning, and artificial intelligence. Google’s expertise in these areas makes GCP a preferred choice for data-intensive and AI-driven applications.

  • Strengths: Superior data analytics and AI capabilities, Kubernetes (container management), competitive pricing.
  • Use Case: Spotify uses GCP for its data analytics and machine learning needs, processing massive amounts of data to deliver personalized music recommendations.

Quote: “Google Cloud Platform excels in data analytics and AI, providing businesses with the tools to harness the power of their data.” – Thomas Kurian, CEO of Google Cloud

Conclusion

Cloud computing offers significant benefits in terms of scalability, flexibility, and cost savings. However, to fully realize these benefits and overcome associated challenges, CTOs should adopt hybrid and multi-cloud strategies. By doing so, organizations can optimize workloads, avoid vendor lock-in, enhance resilience, and drive innovation.

As Diane Greene, former CEO of Google Cloud, aptly puts it, “Cloud is not a destination, it’s a journey.” For CTOs, this journey involves continuously evolving strategies to leverage the full potential of cloud technologies while addressing the dynamic needs of their organizations.

Read more blog post on Cloud Infrastructure here : https://renierbotha.com/tag/cloud/

Stay tuned as we continue to explore critical topics in our 10-day blog series, “Navigating the Future: A 10-Day Blog Series on the Evolving Role of the CTO” by Renier Botha.

Visit www.renierbotha.com for more insights and expert advice.

Unleashing the Power of Data Analytics: Integrating Power BI with Azure Data Marts

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Leveraging the right tools can make a significant difference in how organisations harness and interpret their data. Two powerful tools that, when combined, offer unparalleled capabilities are Power BI and Azure Data Marts. In this blog post, we compare and will explore how these tools integrate seamlessly to provide robust, scalable, and high-performance data analytics solutions.

What is a Data Mart

A data mart is a subset of a data warehouse that is focused on a specific business line, team, or department. It contains a smaller, more specific set of data that addresses the particular needs and requirements of the users within that group. Here are some key features and purposes of a data mart:

  • Subject-Specific: Data marts are designed to focus on a particular subject or business area, such as sales, finance, or marketing, making the data more relevant and easier to analyse for users within that domain.
  • Simplified Data Access: By containing a smaller, more focused dataset, data marts simplify data access and querying processes, allowing users to retrieve and analyse information more efficiently.
  • Improved Performance: Because data marts deal with smaller datasets, they generally offer better performance in terms of data retrieval and processing speed compared to a full-scale data warehouse.
  • Cost-Effective: Building a data mart can be less costly and quicker than developing an enterprise-wide data warehouse, making it a practical solution for smaller organisations or departments with specific needs.
  • Flexibility: Data marts can be tailored to the specific requirements of different departments or teams, providing customised views and reports that align with their unique business processes.

There are generally two types of data marts:

  • Dependent Data Mart: These are created by drawing data from a central data warehouse. They depend on the data warehouse for their data, which ensures consistency and integration across the organisation.
  • Independent Data Mart: These are standalone systems that are created directly from operational or external data sources without relying on a central data warehouse. They are typically used for departmental or functional reporting.

In summary, data marts provide a streamlined, focused approach to data analysis by offering a subset of data relevant to specific business areas, thereby enhancing accessibility, performance, and cost-efficiency.

Understanding the Tools: Power BI and Azure Data Marts

Power BI Datamarts:
Power BI is a leading business analytics service by Microsoft that enables users to create interactive reports and dashboards. With its user-friendly interface and powerful data transformation capabilities, Power BI allows users to connect to a wide range of data sources, shape the data as needed, and share insights across their organisation. Datamarts in Power BI Premium are self-service analytics solutions that allow users to store and explore data in a fully managed database.

Azure Data Marts:
Azure Data Marts are a component of Azure Synapse Analytics, designed to handle large volumes of structured and semi-structured data. They provide high-performance data storage and processing capabilities, leveraging the power of distributed computing to ensure efficient query performance and scalability.

Microsoft Fabric:

In Sep’23, as a significant step forward for data management and analytics, Microsoft has bundled Power BI and Azure Synapse Analytics (including Azure Data Marts) as part of its Fabric SaaS suite. This comprehensive solution, known as Microsoft Fabric, represents the next evolution in data management. By integrating these powerful tools within a single suite, Microsoft Fabric provides a unified platform that enhances data connectivity, transformation, and visualisation. Users can now leverage the full capabilities of Power BI and Azure Data Marts seamlessly, driving more efficient data workflows, improved performance, and advanced analytics capabilities, all within one cohesive ecosystem. This integration is set to revolutionise how organisations handle their data, enabling deeper insights and more informed decision-making.

The Synergy: How Power BI and Azure Data Marts Work Together

Integration and Compatibility

  1. Data Connectivity:
    Power BI offers robust connectivity options that seamlessly link it with Azure Data Marts. Users can choose between Direct Query and Import modes, ensuring they can access and analyse their data in real-time or work with offline datasets for faster querying.
  2. Data Transformation:
    Using Power Query within Power BI, users can clean, transform, and shape data imported from Azure Data Warehouses or Azure Data Marts into PowerBI Data Marts. This ensures that data is ready for analysis and visualisation, enabling more accurate and meaningful insights.
  3. Visualisation and Reporting:
    With the transformed data, Power BI allows users to create rich, interactive reports and dashboards. These visualisations can then be shared across the organisation, promoting data-driven decision-making.

Workflow Integration

The integration of Power BI with Azure Data Marts follows a streamlined workflow:

  • Data Storage: Store large datasets in Azure Data Marts, leveraging its capacity to handle complex queries and significant data volumes.
  • ETL Processes: Utilise Power Query or Azure Data Factory or other ETL tools to manage data extraction, transformation, and loading into the Data Mart.
  • Connecting to Power BI: Link Power BI to Azure Data Marts using its robust connectivity options.
  • Further Data Transformation: Refine the data within Power BI using Power Query to ensure it meets the analytical needs.
  • Creating Visualisations: Develop interactive and insightful reports and dashboards in Power BI.
  • Sharing Insights: Distribute the reports and dashboards to stakeholders, fostering a culture of data-driven insights.

Benefits of the Integration

  • Scalability: Azure Data Marts provide scalable storage and processing, while Power BI scales visualisation and reporting.
  • Performance: Enhanced performance through optimised queries and real-time data access.
  • Centralised Data Management: Ensures data consistency and governance, leading to accurate and reliable reporting.
  • Advanced Analytics: Combining both tools allows for advanced analytics, including machine learning and AI, through integrated Azure services.

In-Depth Comparison: Power BI Data Mart vs Azure Data Mart

Comparing the features, scalability, and resilience of a PowerBI Data Mart and an Azure Data Mart or Warehouse reveals distinct capabilities suited to different analytical needs and scales. Here’s a detailed comparison:

Features

PowerBI Data Mart:

  • Integration: Seamlessly integrates with Power BI for reporting and visualisation.
  • Ease of Use: User-friendly interface designed for business users with minimal technical expertise.
  • Self-service: Enables self-service analytics, allowing users to create their own data models and reports.
  • Data Connectivity: Supports connections to various data sources, including cloud-based and on-premises systems.
  • Data Transformation: Built-in ETL (Extract, Transform, Load) capabilities for data preparation.
  • Real-time Data: Can handle near-real-time data through direct query mode.
  • Collaboration: Facilitates collaboration with sharing and collaboration features within Power BI.

Azure Data Warehouse (Azure Synapse Analytics / Microsoft Fabric Data Warehouse):

  • Data Integration: Deep integration with other Azure services (Azure Data Factory, Azure Machine Learning, etc.).
  • Data Scale: Capable of handling massive volumes of data with distributed computing architecture.
  • Performance: Optimised for large-scale data processing with high-performance querying.
  • Advanced Analytics: Supports advanced analytics with integration for machine learning and AI.
  • Security: Robust security features including encryption, threat detection, and advanced network security.
  • Scalability: On-demand scalability to handle varying workloads.
  • Cost Management: Pay-as-you-go pricing model, optimising costs based on usage.

Scalability

PowerBI Data Mart:

  • Scale: Generally suitable for small to medium-sized datasets.
  • Performance: Best suited for departmental or team-level reporting and analytics.
  • Limits: Limited scalability for very large datasets or complex analytical queries.

Azure Data Warehouse:

  • Scale: Designed for enterprise-scale data volumes, capable of handling petabytes of data.
  • Performance: High scalability with the ability to scale compute and storage independently.
  • Elasticity: Automatic scaling and workload management for optimised performance.

Resilience

PowerBI Data Mart:

  • Redundancy: Basic redundancy features, reliant on underlying storage and compute infrastructure.
  • Recovery: Limited disaster recovery features compared to enterprise-grade systems.
  • Fault Tolerance: Less fault-tolerant for high-availability requirements.

Azure Data Warehouse:

  • Redundancy: Built-in redundancy across multiple regions and data centres.
  • Recovery: Advanced disaster recovery capabilities, including geo-replication and automated backups.
  • Fault Tolerance: High fault tolerance with automatic failover and high availability.

Support for Schemas

Both PowerBI Data Mart and Azure Data Warehouse support the following schemas:

  • Star Schema:
    • PowerBI Data Mart: Supports star schema for simplified reporting and analysis.
    • Azure Data Warehouse: Optimised for star schema, enabling efficient querying and performance.
  • Snowflake Schema:
    • PowerBI Data Mart: Can handle snowflake schema, though complexity may impact performance.
    • Azure Data Warehouse: Well-suited for snowflake schema, with advanced query optimisation.
  • Galaxy Schema:
    • PowerBI Data Mart: Limited support, better suited for simpler schemas.
    • Azure Data Warehouse: Supports galaxy schema, suitable for complex and large-scale data models.

Summary

  • PowerBI Data Mart: Ideal for small to medium-sized businesses or enterprise departmental analytics with a focus on ease of use, self-service, and integration with Power BI.
  • Azure Data Warehouse: Best suited for large enterprises requiring scalable, resilient, and high-performance data warehousing solutions with advanced analytics capabilities.

This table provides a clear comparison of the features, scalability, resilience, and schema support between PowerBI Data Mart and Azure Data Warehouse.

Feature/AspectPowerBI Data MartAzure Data Warehouse (Azure Synapse Analytics)
IntegrationSeamless with Power BIDeep integration with Azure services
Ease of UseUser-friendly interfaceRequires technical expertise
Self-serviceEnables self-service analyticsSupports advanced analytics
Data ConnectivityVarious data sourcesWide range of data sources
Data TransformationBuilt-in ETL capabilitiesAdvanced ETL with Azure Data Factory
Real-time DataSupports near-real-time dataCapable of real-time analytics
CollaborationSharing and collaboration featuresCollaboration through Azure ecosystem
Data ScaleSmall to medium-sized datasetsEnterprise-scale, petabytes of data
PerformanceSuitable for departmental analyticsHigh-performance querying
Advanced AnalyticsBasic analyticsAdvanced analytics and AI integration
SecurityBasic security featuresRobust security with encryption and threat detection
ScalabilityLimited scalabilityOn-demand scalability
Cost ManagementIncluded in Power BI subscriptionPay-as-you-go pricing model
RedundancyBasic redundancyBuilt-in redundancy across regions
RecoveryLimited disaster recoveryAdvanced disaster recovery capabilities
Fault ToleranceLess fault-tolerantHigh fault tolerance and automatic failover
Star Schema SupportSupportedOptimised support
Snowflake Schema SupportSupportedWell-suited and optimised
Galaxy Schema SupportLimited supportSupported for complex models
Datamart: PowerBI vs Azure

Conclusion

Integrating Power BI with Azure Data Marts is a powerful strategy for any organisation looking to enhance its data analytics capabilities. Both platforms support star, snowflake, and galaxy schemas, but Azure Data Warehouse provides better performance and scalability for complex and large-scale data models. The seamless integration offers a robust, scalable, and high-performance solution, enabling users to gain deeper insights and make informed decisions.

Additionally, with Power BI and Azure Data Marts now bundled as part of Microsoft’s Fabric SaaS suite, users benefit from a unified platform that enhances data connectivity, transformation, visualisation, scalability and resilience, further revolutionising data management and analytics.

By leveraging the strengths of Microsoft’s Fabric, organisations can unlock the full potential of their data, driving innovation and success in today’s data-driven world.

Mastering Data Cataloguing: A Comprehensive Guide for Modern Businesses

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Introduction: The Importance of Data Cataloguing in Modern Business

With big data now mainstream, managing vast amounts of information has become a critical challenge for businesses across the globe. Effective data management transcends mere data storage, focusing equally on accessibility and governability. “Data cataloguing is critical because it not only organizes data but also makes it accessible and actionable,” notes Susan White, a renowned data management strategist. This process is a vital component of any robust data management strategy.

Today, we’ll explore the necessary steps to establish a successful data catalogue. We’ll also highlight some industry-leading tools that can help streamline this complex process. “A well-implemented data catalogue is the backbone of data-driven decision-making,” adds Dr. Raj Singh, an expert in data analytics. “It provides the transparency needed for businesses to effectively use their data, ensuring compliance and enhancing operational efficiency.”

By integrating these expert perspectives, we aim to provide a comprehensive overview of how data cataloguing can significantly benefit your organization, supporting more informed decision-making and strategic planning.

Understanding Data Cataloguing

Data cataloguing involves creating a central repository that organises, manages, and maintains an organisation’s data to make it easily discoverable and usable. It not only enhances data accessibility but also supports compliance and governance, making it an indispensable tool for businesses.

Step-by-Step Guide to Data Cataloguing

1. Define Objectives and Scope

Firstly, identify what you aim to achieve with your data catalogue. Goals may include compliance, improved data discovery, or better data governance. Decide on the scope – whether it’s for the entire enterprise or specific departments.

2. Gather Stakeholder Requirements

Involve stakeholders such as data scientists, IT professionals, and business analysts early in the process. Understanding their needs – from search capabilities to data lineage – is crucial for designing a functional catalogue.

3. Choose the Right Tools

Selecting the right tools is critical for effective data cataloguing. Consider platforms like Azure Purview, which offers extensive metadata management and governance capabilities within the Microsoft ecosystem. For those embedded in the Google Cloud Platform, Google Cloud Data Catalog provides powerful search functionalities and automated schema management. Meanwhile, AWS Glue Data Catalog is a great choice for AWS users, offering seamless integration with other AWS services. More detail on tooling below.

4. Develop a Data Governance Framework

Set clear policies on who can access and modify the catalogue. Standardise how metadata is collected, stored, and updated to ensure consistency and reliability.

5. Collect and Integrate Data

Document all data sources and use automation tools to extract metadata. This step reduces manual errors and saves significant time.

6. Implement Metadata Management

Decide on the types of metadata to catalogue (technical, business, operational) and ensure consistency in its description and format.

  • Business Metadata: This type of metadata provides context to data by defining commonly used terms in a way that is independent of technical implementation. The Data Management Body of Knowledge (DMBoK) notes that business metadata primarily focuses on the nature and condition of the data, incorporating elements related to Data Governance.
  • Technical Metadata: This metadata supplies computer systems with the necessary information about data’s format and structure. It includes details such as physical database tables, access restrictions, data models, backup procedures, mapping specifications, data lineage, and more.
  • Operational Metadata: As defined by the DMBoK, operational metadata pertains to the specifics of data processing and access. This includes information such as job execution logs, data sharing policies, error logs, audit trails, maintenance plans for multiple versions, archiving practices, and retention policies.

7. Populate the Catalogue

Use automated tools (see section on tooling below) and manual processes to populate the catalogue. Regularly verify the integrity of the data to ensure accuracy.

8. Enable Data Discovery and Access

A user-friendly interface is key to enhancing engagement and making data discovery intuitive. Implement robust security measures to protect sensitive information.

9. Train Users

Provide comprehensive training and create detailed documentation to help users effectively utilise the catalogue.

10. Monitor and Maintain

Keep the catalogue updated with regular reviews and revisions. Establish a feedback loop to continuously improve functionality based on user input.

11. Evaluate and Iterate

Use metrics to assess the impact of the catalogue and make necessary adjustments to meet evolving business needs.

Data Catalogue’s Value Proposition

Data catalogues are critical assets in modern data management, helping businesses harness the full potential of their data. Here are several real-life examples illustrating how data catalogues deliver value to businesses across various industries:

  • Financial Services: Improved Compliance and Risk Management – A major bank implemented a data catalogue to manage its vast data landscape, which includes data spread across different systems and geographies. The data catalogue enabled the bank to enhance its data governance practices, ensuring compliance with global financial regulations such as GDPR and SOX. By providing a clear view of where and how data is stored and used, the bank was able to effectively manage risks and respond to regulatory inquiries quickly, thus avoiding potential fines and reputational damage.
  • Healthcare: Enhancing Patient Care through Data Accessibility – A large healthcare provider used a data catalogue to centralise metadata from various sources, including electronic health records (EHR), clinical trials, and patient feedback systems. This centralisation allowed healthcare professionals to access and correlate data more efficiently, leading to better patient outcomes. For instance, by analysing a unified view of patient data, researchers were able to identify patterns that led to faster diagnoses and more personalised treatment plans.
  • Retail: Personalisation and Customer Experience Enhancement – A global retail chain implemented a data catalogue to better manage and analyse customer data collected from online and in-store interactions. With a better-organised data environment, the retailer was able to deploy advanced analytics to understand customer preferences and shopping behaviour. This insight enabled the retailer to offer personalised shopping experiences, targeted marketing campaigns, and optimised inventory management, resulting in increased sales and customer satisfaction.
  • Telecommunications: Network Optimisation and Fraud Detection – A telecommunications company utilised a data catalogue to manage data from network traffic, customer service interactions, and billing systems. This comprehensive metadata management facilitated advanced analytics applications for network optimisation and fraud detection. Network engineers were able to predict and mitigate network outages before they affected customers, while the fraud detection teams used insights from integrated data sources to identify and prevent billing fraud effectively.
  • Manufacturing: Streamlining Operations and Predictive Maintenance – In the manufacturing sector, a data catalogue was instrumental for a company specialising in high-precision equipment. The catalogue helped integrate data from production line sensors, machine logs, and quality control to create a unified view of the manufacturing process. This integration enabled predictive maintenance strategies that reduced downtime by identifying potential machine failures before they occurred. Additionally, the insights gained from the data helped streamline operations, improve product quality, and reduce waste.

These examples highlight how a well-implemented data catalogue can transform data into a strategic asset, enabling more informed decision-making, enhancing operational efficiencies, and creating a competitive advantage in various industry sectors.

A data catalog is an organized inventory of data assets in an organization, designed to help data professionals and business users find and understand data. It serves as a critical component of modern data management and governance frameworks, facilitating better data accessibility, quality, and understanding. Below, we discuss the key components of a data catalog and provide examples of the types of information and features that are typically included.

Key Components of a Data Catalog

  1. Metadata Repository
    • Description: The core of a data catalog, containing detailed information about various data assets.
    • Examples: Metadata could include the names, types, and descriptions of datasets, data schemas, tables, and fields. It might also contain tags, annotations, and extended properties like data type, length, and nullable status.
  2. Data Dictionary
    • Description: A descriptive list of all data items in the catalog, providing context for each item.
    • Examples: For each data element, the dictionary would provide a clear definition, source of origin, usage guidelines, and information about data sensitivity and ownership.
  3. Data Lineage
    • Description: Visualization or documentation that explains where data comes from, how it moves through systems, and how it is transformed.
    • Examples: Lineage might include diagrams showing data flow from one system to another, transformations applied during data processing, and dependencies between datasets.
  4. Search and Discovery Tools
    • Description: Mechanisms that allow users to easily search for and find data across the organization.
    • Examples: Search capabilities might include keyword search, faceted search (filtering based on specific attributes), and full-text search across metadata descriptions.
  5. User Interface
    • Description: The front-end application through which users interact with the data catalog.
    • Examples: A web-based interface that provides a user-friendly dashboard to browse, search, and manage data assets.
  6. Access and Security Controls
    • Description: Features that manage who can view or edit data in the catalog.
    • Examples: Role-based access controls that limit users to certain actions based on their roles, such as read-only access for some users and edit permissions for others.
  7. Integration Capabilities
    • Description: The ability of the data catalog to integrate with other tools and systems in the data ecosystem.
    • Examples: APIs that allow integration with data management tools, BI platforms, and data lakes, enabling automated metadata updates and interoperability.
  8. Quality Metrics
    • Description: Measures and indicators related to the quality of data.
    • Examples: Data quality scores, reports on data accuracy, completeness, consistency, and timeliness.
  9. Usage Tracking and Analytics
    • Description: Tools to monitor how and by whom the data assets are accessed and used.
    • Examples: Logs and analytics that track user queries, most accessed datasets, and patterns of data usage.
  10. Collaboration Tools
    • Description: Features that facilitate collaboration among users of the data catalog.
    • Examples: Commenting capabilities, user forums, and shared workflows that allow users to discuss data, share insights, and collaborate on data governance tasks.
  11. Organisational Framework and Structure
    • The structure of an organisation itself is not typically a direct component of a data catalog. However, understanding and aligning the data catalog with the organizational structure is crucial for several reasons:
      • Role-Based Access Control: The data catalog often needs to reflect the organizational hierarchy or roles to manage permissions effectively. This involves setting up access controls that align with job roles and responsibilities, ensuring that users have appropriate access to data assets based on their position within the organization.
      • Data Stewardship and Ownership: The data catalog can include information about data stewards or owners who are typically assigned according to the organizational structure. These roles are responsible for the quality, integrity, and security of the data, and they often correspond to specific departments or business units.
      • Customization and Relevance: The data catalog can be customized to meet the specific needs of different departments or teams within the organization. For instance, marketing data might be more accessible and prominently featured for the marketing department in the catalog, while financial data might be prioritized for the finance team.
      • Collaboration and Communication: Understanding the organizational structure helps in designing the collaboration features of the data catalog. It can facilitate better communication and data sharing practices among different parts of the organization, promoting a more integrated approach to data management.
    • In essence, while the organisational structure isn’t stored as a component in the data catalog, it profoundly influences how the data catalog is structured, accessed, and utilised. The effectiveness of a data catalog often depends on how well it is tailored and integrated into the organizational framework, helping ensure that the right people have the right access to the right data at the right time.

Example of a Data Catalog in Use

Imagine a large financial institution that uses a data catalog to manage its extensive data assets. The catalog includes:

  • Metadata Repository: Contains information on thousands of datasets related to transactions, customer interactions, and compliance reports.
  • Data Dictionary: Provides definitions and usage guidelines for key financial metrics and customer demographic indicators.
  • Data Lineage: Shows the flow of transaction data through various security and compliance checks before it is used for reporting.
  • Search and Discovery Tools: Enable analysts to find and utilize specific datasets for developing insights into customer behavior and market trends.
  • Quality Metrics: Offer insights into the reliability of datasets used for critical financial forecasting.

By incorporating these components, the institution ensures that its data is well-managed, compliant with regulations, and effectively used to drive business decisions.

Tiveness of a data catalog often depends on how well it is tailored and integrated into the organisational framework, helping ensure that the right people have the right access to the right data at the right time.

Tooling

For organizations looking to implement data cataloging in cloud environments, the major cloud providers – Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS) – each offer their own specialised tools.

Here’s a comparison table that summarises the key features, descriptions, and use cases of data cataloging tools offered by Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS):

FeatureAzure PurviewGoogle Cloud Data CatalogAWS Glue Data Catalog
DescriptionA unified data governance service that automates the discovery of data and cataloging. It helps manage and govern on-premise, multi-cloud, and SaaS data.A fully managed and scalable metadata management service that enhances data discovery and understanding within Google Cloud.A central repository that stores structural and operational metadata, integrating with other AWS services.
Key Features– Automated data discovery and classification.
– Data lineage for end-to-end data insight.
– Integration with Azure services like Azure Data Lake, SQL Database, and Power BI.		– Metadata storage for Google Cloud and external data sources.
– Advanced search functionality using Google Search technology.
– Automatic schema management and discovery.		– Automatic schema discovery and generation.
– Serverless design, scales with data.
– Integration with AWS services like Amazon Athena, Amazon EMR, and Amazon Redshift.
Use CaseBest for organizations deeply integrated into the Microsoft ecosystem, seeking comprehensive governance and compliance capabilities.Ideal for businesses using multiple Google Cloud services, needing a simple, integrated approach to metadata management.Suitable for AWS-centric environments that require a robust, scalable solution for ETL jobs and data querying.
Data Catalogue Tooling Comparison

This table provides a quick overview to help you compare the offerings and decide which tool might be best suited for your organizational needs based on the environment you are most invested in.

Conclusion

Implementing a data catalogue can dramatically enhance an organisation’s ability to manage data efficiently. By following these steps and choosing the right tools, businesses can ensure their data assets are well-organised, easily accessible, and securely governed. Whether you’re part of a small team or a large enterprise, embracing these practices can lead to more informed decision-making and a competitive edge in today’s data-driven world.

Optimising Cloud Management: A Comprehensive Comparison of Bicep and Terraform for Azure Deployment

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In the evolutionary landscape of cloud computing, the ability to deploy and manage infrastructure efficiently is paramount. Infrastructure as Code (IaC) has emerged as a pivotal practice, enabling developers and IT operations teams to automate the provisioning of infrastructure through code. This practice not only speeds up the deployment process but also enhances consistency, reduces the potential for human error, and facilitates scalability and compliance.

Among the tools at the forefront of this revolution are Bicep and Terraform, both of which are widely used for managing resources on Microsoft Azure, one of the leading cloud service platforms. Bicep, developed by Microsoft, is designed specifically for Azure, offering a streamlined approach to managing Azure resources. On the other hand, Terraform, developed by HashiCorp, provides a more flexible, multi-cloud solution, capable of handling infrastructure across various cloud environments including Azure, AWS, and Google Cloud.

The choice between Bicep and Terraform can significantly influence the efficiency and effectiveness of cloud infrastructure management. This article delves into a detailed comparison of these two tools, exploring their capabilities, ease of use, and best use cases to help you make an informed decision that aligns with your organisational needs and cloud strategies.

Bicep and Terraform are both popular Infrastructure as Code (IaC) tools used to manage and provision infrastructure, especially for cloud platforms like Microsoft Azure. Here’s a detailed comparison of the two, focusing on key aspects such as design philosophy, ease of use, community support, and integration capabilities:

  • Language and Syntax
    • Bicep:
      Bicep is a domain-specific language (DSL) developed by Microsoft specifically for Azure. Its syntax is cleaner and more concise compared to ARM (Azure Resource Manager) templates. Bicep is designed to be easy to learn for those familiar with ARM templates, offering a declarative syntax that directly transcompiles into ARM templates.
    • Terraform:
      Terraform uses its own configuration language called HashiCorp Configuration Language (HCL), which is also declarative. HCL is known for its human-readable syntax and is used to manage a wide variety of services beyond just Azure. Terraform’s language is more verbose compared to Bicep but is powerful in expressing complex configurations.
  • Platform Support
    • Bicep:
      Bicep is tightly integrated with Azure and is focused solely on Azure resources. This means it has excellent support for new Azure features and services as soon as they are released.
    • Terraform:
      Terraform is platform-agnostic and supports multiple providers including Azure, AWS, Google Cloud, and many others. This makes it a versatile tool if you are managing multi-cloud environments or need to handle infrastructure across different cloud platforms.
  • State Management
    • Bicep:
      Bicep relies on ARM for state management. Since ARM itself manages the state of resources, Bicep does not require a separate mechanism to keep track of resource states. This can simplify operations but might offer less control compared to Terraform.
    • Terraform:
      Terraform maintains its own state file which tracks the state of managed resources. This allows for more complex dependency tracking and precise state management but requires careful handling, especially in team environments to avoid state conflicts.
  • Tooling and Integration
    • Bicep:
      Bicep integrates seamlessly with Azure DevOps and GitHub Actions for CI/CD pipelines, leveraging native Azure tooling and extensions. It is well-supported within the Azure ecosystem, including integration with Azure Policy and other governance tools.
    • Terraform:
      Terraform also integrates well with various CI/CD tools and has robust support for modules which can be shared across teams and used to encapsulate complex setups. Terraform’s ecosystem includes Terraform Cloud and Terraform Enterprise, which provide advanced features for teamwork and governance.
  • Community and Support
    • Bicep:
      As a newer and Azure-specific tool, Bicep’s community is smaller but growing. Microsoft actively supports and updates Bicep. The community is concentrated around Azure users.
    • Terraform:
      Terraform has a large and active community with a wide range of custom providers and modules contributed by users around the world. This vast community support makes it easier to find solutions and examples for a variety of use cases.
  • Configuration as Code (CaC)
    • Bicep and Terraform:
      Both tools support Configuration as Code (CaC) principles, allowing not only the provisioning of infrastructure but also the configuration of services and environments. They enable codifying setups in a manner that is reproducible and auditable.

This table outlines key differences between Bicep and Terraform (outlined above), helping you to determine which tool might best fit your specific needs, especially in relation to deploying and managing resources in Microsoft Azure for Infrastructure as Code (IaC) and Configuration as Code (CaC) development.

FeatureBicepTerraform
Language & SyntaxSimple, concise DSL designed for Azure.HashiCorp Configuration Language (HCL), versatile and expressive.
Platform SupportAzure-specific with excellent support for Azure features.Multi-cloud support, including Azure, AWS, Google Cloud, etc.
State ManagementUses Azure Resource Manager; no separate state management needed.Manages its own state file, allowing for complex configurations and dependency tracking.
Tooling & IntegrationDeep integration with Azure services and CI/CD tools like Azure DevOps.Robust support for various CI/CD tools, includes Terraform Cloud for advanced team functionalities.
Community & SupportSmaller, Azure-focused community. Strong support from Microsoft.Large, active community. Extensive range of modules and providers available.
Use CaseIdeal for exclusive Azure environments.Suitable for complex, multi-cloud environments.

Conclusion

Bicep might be more suitable if your work is focused entirely on Azure due to its simplicity and deep integration with Azure services. Terraform, on the other hand, would be ideal for environments where multi-cloud support is required, or where more granular control over infrastructure management and versioning is necessary. Each tool has its strengths, and the choice often depends on specific project requirements and the broader technology ecosystem in which your infrastructure operates.

Cloud Provider Showdown: Unravelling Data, Analytics and Reporting Services for Medallion Architecture Lakehouse

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Cloud Wars: A Deep Dive into Data, Analytics and Reporting Services for Medallion Architecture Lakehouse in AWS, Azure, and GCS

Introduction

Crafting a medallion architecture lakehouse demands precision and foresight. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) emerge as juggernauts, each offering a rich tapestry of data and reporting services. This blog post delves into the intricacies of these offerings, unravelling the nuances that can influence your decision-making process for constructing a medallion architecture lakehouse that stands the test of time.

1. Understanding Medallion Architecture: Where Lakes and Warehouses Converge

Medallion architecture represents the pinnacle of data integration, harmonising the flexibility of data lakes with the analytical prowess of data warehouses, combined forming a lakehouse. By fusing these components seamlessly, organisations can facilitate efficient storage, processing, and analysis of vast and varied datasets, setting the stage for data-driven decision-making.

The medallion architecture is a data design pattern used to logically organise data in a lakehouse, with the goal of incrementally and progressively improving the structure and quality of data as it flows through each layer of the architecture. The architecture describes a series of data layers that denote the quality of data stored in the lakehouse. It is highly recommended, by Microsoft and Databricks, to take a multi-layered approach to building a single source of truth (golden source) for enterprise data products. This architecture guarantees atomicity, consistency, isolation, and durability as data passes through multiple layers of validations and transformations before being stored in a layout optimised for efficient analytics. The terms bronze (raw), silver (validated), and gold (enriched) describe the quality of the data in each of these layers. It is important to note that this medallion architecture does not replace other dimensional modelling techniques. Schemas and tables within each layer can take on a variety of forms and degrees of normalisation depending on the frequency and nature of data updates and the downstream use cases for the data.

2. Data Services

Amazon Web Services (AWS):

  • Storage:
    • Amazon S3: A scalable object storage service, ideal for storing and retrieving any amount of data.
  • ETL/ELT:
    • AWS Glue: An ETL service that automates the process of discovering, cataloguing, and transforming data.
  • Data Warehousing:
    • Amazon Redshift: A fully managed data warehousing service that makes it simple and cost-effective to analyse all your data using standard SQL and your existing Business Intelligence (BI) tools.

Microsoft Azure:

  • Storage:
    • Azure Blob Storage: A massively scalable object storage for unstructured data.
  • ETL/ELT:
    • Azure Data Factory: A cloud-based data integration service for orchestrating and automating data workflows.
  • Data Warehousing
    • Azure Synapse Analytics (formerly Azure SQL Data Warehouse): Integrates big data and data warehousing. It allows you to analyse both relational and non-relational data at petabyte-scale.

Google Cloud Platform (GCP):

  • Storage:
    • Google Cloud Storage: A unified object storage service with strong consistency and global scalability.
  • ETL/ELT:
    • Cloud Dataflow: A fully managed service for stream and batch processing.
  • Data Warehousing:
    • BigQuery: A fully-managed, serverless, and highly scalable data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure.

3 . Analytics

Google Cloud Platform (GCP):

  • Dataproc: A fast, easy-to-use, fully managed cloud service for running Apache Spark and Apache Hadoop clusters.
  • Dataflow: A fully managed service for stream and batch processing.
  • Bigtable: A NoSQL database service for large analytical and operational workloads.
  • Pub/Sub: A messaging service for event-driven systems and real-time analytics.

Microsoft Azure:

  • Azure Data Lake Analytics: Allows you to run big data analytics and provides integration with Azure Data Lake Storage.
  • Azure HDInsight: A cloud-based service that makes it easy to process big data using popular frameworks like Hadoop, Spark, Hive, and more.
  • Azure Databricks: An Apache Spark-based analytics platform that provides collaborative environment and tools for data scientists, engineers, and analysts.
  • Azure Stream Analytics: Helps in processing and analysing real-time streaming data.
  • Azure Synapse Analytics: An analytics service that brings together big data and data warehousing.

Amazon Web Services (AWS):

  • Amazon EMR (Elastic MapReduce): A cloud-native big data platform, allowing processing of vast amounts of data quickly and cost-effectively across resizable clusters of Amazon EC2 instances.
  • Amazon Kinesis: Helps in real-time processing of streaming data at scale.
  • Amazon Athena: A serverless, interactive analytics service that provides a simplified and flexible way to analyse petabytes of data where it lives in Amazon S3 using standard SQL expressions. 

4. Report Writing Services: Transforming Data into Insights

  • AWS QuickSight: A business intelligence service that allows creating interactive dashboards and reports.
  • Microsoft Power BI: A suite of business analytics tools for analysing data and sharing insights.
  • Google Data Studio: A free and collaborative tool for creating interactive reports and dashboards.

5. Comparison Summary:

  • Storage: All three providers offer reliable and scalable storage solutions. AWS S3, Azure Blob Storage, and GCS provide similar functionalities for storing structured and unstructured data.
  • ETL/ELT: AWS Glue, Azure Data Factory, and Cloud Dataflow offer ETL/ELT capabilities, allowing you to transform and prepare data for analysis.
  • Data Warehousing: Amazon Redshift, Azure Synapse Analytics, and BigQuery are powerful data warehousing solutions that can handle large-scale analytics workloads.
  • Analytics: Azure, AWS, and GCP are leading cloud service providers, each offering a comprehensive suite of analytics services tailored to diverse data processing needs. The choice between them depends on specific project needs, existing infrastructure, and the level of expertise within the development team.
  • Report Writing: QuickSight, Power BI, and Data Studio offer intuitive interfaces for creating interactive reports and dashboards.
  • Integration: AWS, Azure, and GCS services can be integrated within their respective ecosystems, providing seamless connectivity and data flow between different components of the lakehouse architecture. Azure integrates well with other Microsoft services. AWS has a vast ecosystem and supports a wide variety of third-party integrations. GCP is known for its seamless integration with other Google services and tools.
  • Cost: Pricing models vary across providers and services. It’s essential to compare the costs based on your specific usage patterns and requirements. Each provider offers calculators to estimate costs.
  • Ease of Use: All three platforms offer user-friendly interfaces and APIs. The choice often depends on the specific needs of the project and the familiarity of the development team.
  • Scalability: All three platforms provide scalability options, allowing you to scale your resources up or down based on demand.
  • Performance: Performance can vary based on the specific service and configuration. It’s recommended to run benchmarks or tests based on your use case to determine the best-performing platform for your needs.

6. Decision-Making Factors: Integration, Cost, and Expertise

  • Integration: Evaluate how well the services integrate within their respective ecosystems. Seamless integration ensures efficient data flow and interoperability.
  • Cost Analysis: Conduct a detailed analysis of pricing structures based on storage, processing, and data transfer requirements. Consider potential scalability and growth factors in your evaluation.
  • Team Expertise: Assess your team’s proficiency with specific tools. Adequate training resources and community support are crucial for leveraging the full potential of chosen services.

Conclusion: Navigating the Cloud Maze for Medallion Architecture Excellence

Selecting the right combination of data and reporting services for your medallion architecture lakehouse is not a decision to be taken lightly. AWS, Azure, and GCP offer powerful solutions, each tailored to different organisational needs. By comprehensively evaluating your unique requirements against the strengths of these platforms, you can embark on your data management journey with confidence. Stay vigilant, adapt to innovations, and let your data flourish in the cloud – ushering in a new era of data-driven excellence.

Case Study: Renier Botha’s Leadership in Rivus’ Digital Strategy Implementation

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Introduction

Rivus Fleet Solutions, a leading provider of fleet management services, embarked on a significant digital transformation to enhance its operational efficiencies and customer services. Renier Botha, a seasoned IT executive, played a crucial role in this transformation, focusing on three major areas: upgrading key database infrastructure, leading innovative product development, and managing critical transition projects. This case study explores how Botha’s efforts have propelled Rivus towards a more digital future.

Background

Renier Botha, known for his expertise in digital strategy and IT management, took on the challenge of steering Rivus through multiple complex digital initiatives. The scope of his work covered:

  1. Migration of Oracle 19c enterprise database,
  2. Development of a cross-platform mobile application, and
  3. Management of the service transition project with BT & Openreach.

Oracle 19c Enterprise Upgrade Migration

Objective: Upgrade the core database systems to Oracle 19c to ensure enhanced performance, improved security, and extended support.

Approach:
Botha employed a robust programme management approach to handle the complexities of upgrading the enterprise-wide database system. This involved:

  • Detailed planning and risk management to mitigate potential downtime,
  • Coordination with internal IT teams and external Oracle consultants,
  • Comprehensive testing phases to ensure system compatibility and performance stability.

Outcome:
The successful migration to Oracle 19c provided Rivus with a more robust and secure database environment, enabling better data management and scalability options for future needs. This foundational upgrade was crucial for supporting other digital initiatives within the company.

Cross-Platform Mobile Application Development

Objective: Develop a mobile application to facilitate seamless digital interaction between Rivus and its customers, enhancing service accessibility and efficiency.

Approach:
Botha led the product development team through:

  • Identifying key user requirements by engaging with stakeholders,
  • Adopting agile methodologies for rapid and iterative development,
  • Ensuring cross-platform compatibility to maximise user reach.

Outcome:
The new mobile application promissed to significantly transformed how customers interacted with Rivus, providing them with the ability to manage fleet services directly from their devices. This not only improved customer satisfaction but also streamlined Rivus’ operational processes.

BT & Openreach Exit Project Management

Objective: Manage the transition of fleet technology services of BT & Openreach ensuring minimal service disruption.

Approach:
This project was complex, involving intricate service agreements and technical dependencies. Botha’s strategy included:

  • Detailed project planning and timeline management,
  • Negotiations and coordination with multiple stakeholders from BT, Openreach, and internal teams,
  • Focusing on knowledge transfer and system integrations.

Outcome:
The project was completed efficiently, allowing Rivus to transition control of critical services succesfully and without business disruption.

Conclusion

Renier Botha’s strategic leadership in these projects has been pivotal for Rivus. By effectively managing the Oracle 19c upgrade, he laid a solid technological foundation. The development of the cross-platform mobile app under his guidance directly contributed to improved customer engagement and operational efficiency. Finally, his adept handling of the BT & Openreach transition solidified Rivus’ operational independence. Collectively, these achievements represent a significant step forward in Rivus’ digital strategy, demonstrating Botha’s profound impact on the company’s technological advancement.

Innovation Case Study: Test Automation & Ambit Enterprise Upgrade

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A business case of how technology innovation successfully integrated into the business operations an improved the way of working that supported business success.

  
Areas of Science and TechnologyData Engineering, Computer Science
R&D Start DateDec 2018
R&D End DateSeptember 2019
Competent ProfessionalRenier Botha

 

Overview and Available Baseline Technologies

Within the scope of the project, the competent professionals sought to develop a regression testing framework aimed at testing the work carried out to upgrade the Ambit application[1] from a client service solution to a software as a service solution (SaaS) operating in the Cloud. The test framework developed is now used to define and support any testing initiatives across the Bank. The team also sought to automate the process, however this failed due to lack of existing infrastructure in the Bank. 

Initial attempts to achieve this by way of third-party solution providers, such as Qualitest, were unsuccessful, as these providers were unable to develop a framework or methodology which could be documented and reused across different projects. For this the team sought to develop the framework from the ground up. The project was successfully completed in September 2019. 

Technological Advances

The upgrade would enable access to the system via the internet, meaning users would no longer need a Cisco connection onto the specific servers to engage with the application. The upgrade would also enable the system to be accessed from devices other than a PC or laptop. Business Finance at Shawbrook is comprised of 14 different business units, with each unit having a different product which is captured and processed through Ambit. All the existing functionality, and business specific configuration needed to be transferred into the new Enterprise platform, as well as the migration of all the associated data. The competent professionals at Shawbrook sought to appreciably improve the current application through the following technological advances:

  • Development of an Automated Test Framework which could be used across different projects

Comprehensive, well executed testing is essential for mitigating risks to deployment. Shawbrook did not have a documented, standardised, and proven methodology that could be adopted by different projects to ensure that proper testing practises are incorporated into project delivery. There was a requirement to develop a test framework to plan, manage, govern and support testing across the agreed phases, using tools and practices that help mitigate risks in a cost-effective and commensurate way.

The test team sought to develop a continuous delivery framework, which could be used across all units within Business Finance. The Ambit Enterprise Upgrade was the first project at Shawbrook to adopt this framework, which lead to the development of a regression test pack and the subsequent successful delivery of the Ambit upgrade. The Ambit Enterprise project was the first project within the Bank which was delivered with no issues raised post release.

The development of a regression test pack which would enable automated testing of future changes or upgrades to the Ambit platform

Regression testing is a fundamental part of the software development lifecycle. With the increased popularity of the Agile development methodology, regression testing has taken on added importance. The team at Shawbrook sought to adopt an iterative, Agile approach to software development. 

A manual regression test pack was developed which could be used for future testing without the need for the involvement of business users. This was delivered over three test cycles with the team using the results of each cycle (bugs identified and resolved) to issue new releases. 

173 user paths were captured in the regression test pack, across 14 different divisions within Business Finance. 251 issues were found during testing, with some being within the Ambit application. Identifying and resolving these issues resulted in the advancement of Ambit Enterprise platform itself. This regression test pack can now be used for future changes to the Ambit Enterprise application, as well as future FIS[2] releases, change requests and enhancements, without being dependent on the business users to undertake UAT. The competent professionals at Shawbrook are currently using the regression test pack to test the integration functionality of the Ambit Enterprise platform.

  • Development of a costing tool to generate cost estimates for cloud test environment requirements

In order to resolve issues, solutions need to be tested within test environments. A lack of supply was identified within Shawbrook and there was an initiative to increase supply using the Azure cloud environment. The objective was to increase the capability within Business Finance to manage an Azure flexible hosting environment where necessary test environments could be set up on demand. There was also a requirement to plan and justify the expense of test environment management. The competent professionals sought to develop a costing tool, based on the Azure costing model, which could be used by project managers within Business Application Support (“BAS”) to quickly generate what the environment cost would be on a per day or per hour running basis. Costs were calculated based on the environment specification required and number of running hours required. Environment specification was classified as either “high”, “medium” or “low”. For example, the test environment specification required for a web server is low, an application server is medium while a database server is high. Shawbrook gained knowledge and increase its capability of the use of the Azure cloud environment and as a result are actively using the platform to undertake cloud-based testing.

The above constitutes an advance in knowledge and capability in the field of Data Engineering and Computer Science, as per sections 9 a) and c) of the BEIS Guidelines.

Technological Uncertainties and activities carried out to address them

The following technological uncertainties were encountered while developing the Ambit Enterprise upgrade, mainly pertaining to system uncertainty:

  • Implementation of the new Ambit Enterprise application could disrupt existing business processes

The biggest risks for the programme of change, was the potential disruption of existing business processes due to the implementation of the change without validation of the upgraded application against the existing functionality. This was the primary focus of the risk mitigation process for the project. Following the test phases set out in the test framework would enable a clear understanding of all the residual risks encountered approaching implementation, providing stakeholders with the context required to make a calculated judgement on these risks.

When an issue was identified through testing, a triage process was undertaken to categorise the issues as either a technical issue, or a user issue. User issues were further classified as “training” or “change of business process”. Technical issues were classified as “showstoppers”, “high”, “medium” and “low”. These were further categorised by priority as “must haves” and “won’t haves” in order to get well-defined acceptance criteria for the substantial list of bugs that arose from the testing cycles. In total, 251 technical issues were identified.

The acceptance criteria for the resolution of issues were:

  • A code fix was implemented
    • A business approved work around was implemented
    • The business accepted the risk

All showstoppers were resolved with either a code fix or and an acceptable work around. Configuration issues were within the remit of Shawbrook’s business application support (“BAS”) team to resolve, whilst other issues could only be resolved by the FIS development team. When the application went live, there were no issues raised post release, and all issues present were known and met the acceptance criteria of the business. 

  • Business processes may no longer align with the new web-based application

Since the project was an upgrade, there was the potential for operational impact of existing functionality due to differences between the Ambit client server solution, and the upgraded Ambit Enterprise web-based solution. The BAS team at Shawbrook were required to make changes to the business processes in order to align with the way the Ambit Enterprise solution now operated. Where Shawbrook specific issues could not be resolved through the configuration of the application with the business processes, changes were made to the functionality within Ambit, for example, additional plug-ins were developed for the Sales Portal platform to integrate with the Ambit Enterprise application. 

Because Ambit Enterprise was a web-based application, application and security vulnerabilities needed to be identified so that the correct security level was achieved. Because of this, performance and security testing, which was currently not being executed, needed to be introduced to the test framework. Performance testing also needed to be executed so that speed and stability requirements under the expected workloads were met.

Summary and Conclusions

The team at Shawbrook successfully developed a test framework which could be used across all projects within Business Finance. The development of the test framework lead to the generation of a regression test pack for the Ambit Enterprise upgrade. By undertaking these R&D activities, Shawbrook gained knowledge in the use of Azure Cloud Environment for testing, and increased its automated testing capabilities, enabling the transition to a continuous delivery framework whereby the majority of testing is automated.

[1] Ambit is the asset finance application operating within the business unit, 70-80 percent of transactions on all lending is captured and managed through Ambit

[2] FIS is the Ambit Enterprise vendor

Release Management as a Competitive Advantage

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“Delivery focussed”, “Getting the job done”, “Results driven”, “The proof is in the pudding” – we are all familiar with these phrases and in Information Technology it means getting the solutions into operations through effective Release Management, quickly.

In the increasingly competitive market, where digital is enabling rapid change, time to market is king. Translated into IT terms – you must get your solution into production before the competition does, through an effective ability to do frequent releases. Doing frequent releases benefit teams as features can be validated earlier and bugs detected and resolved rapidly. The smaller iteration cycles provide flexibility, making adjustments to unforeseen scope changes easier and reducing the overall risk of change while rapidly enhancing stability and reliability in the production environment.

IT teams with well governed agile and robust release management practices have a significant competitive advantage. This advantage materialises through self-managed teams consisting of highly skilled technologist who collaborative work according to a team defined release management process enabled by continuous integration and continuous delivery (CICD), that continuously improves through constructive feedback loops and corrective actions.

The process of implementing such agile practices, can be challenging as building software becomes increasingly more complex due to factors such as technical debt, increasing legacy code, resource movements, globally distributed development teams, and the increasing number of platforms to be supported.

To realise this advantage, an organisation must first optimise its release management process and identify the most appropriate platform and release management tools.

Here are three well known trends that every technology team can use to optimise delivery:

1. Agile delivery practises – with automation at the core 

So, you have adopted an agile delivery methodology and you’re having daily scrum meetings – but you know that is not enough. Sprint planning as well as review and retrospection are all essential elements for a successful release, but in order to gain substantial and meaningful deliverables within the time constraints of agile iterations, you need to invest in automation.

An automation ability brings measurable benefits to the delivery team as it reduces the pressure on people in minimising human error and increasing overall productivity and delivery quality into your production environment that shows in key metrics like team velocity. Another benefit automation introduces is consistent and repeatable process, enabling easily scalable teams while reducing errors and release times. Agile delivery practices (see “Executive Summary of 4 commonly used Agile Methodologies“) all embrace and promote the use of automation across the delivery lifecycle, especially in build, test and deployment automation. Proper automation support delivery teams in reducing overhead of time-consuming repetitive tasks in configuration and testing so them can focus on the core of customer centric product/service development with quality build in. Also read “How to Innovate to stay Relevant“; “Agile Software Development – What Business Executives need to know” for further insight in Agile methodologies…

Example:

Code Repository (version Control) –> Automated Integration –> Automated Deployment of changes to Test Environments –> Platform & Environment Changes automated build into Testbed –> Automated Build Acceptance Tests –> Automated Release

When a software developer commits changes to the version control, these changes automatically get integrated with the rest of the modules. Integrated assembles are then automatically deployed to a test environment – changes to the platform or the environment, gets automatically built and deployed on the test bed. Next, build acceptance tests are automatically kicked off, which would include capacity tests, performance, and reliability tests. Developers and/or leads are notified only when something fails. Therefore, the focus remains on core development and not just on other overhead activities. Of course, there will be some manual check points that the release management team will have to pass in order to trigger next the phase, but each activity within this deployment pipeline can be more or less automated. As your software passes all quality checkpoints, product version releases are automatically pushed to the release repository from which new versions can be pulled automatically by systems or downloaded by customers.

Example Technologies:

  • Build Automation:  Ant, Maven, Make
  • Continuous Integration: Jenkins, Cruise Control, Bamboo
  • Test Automation: Silk Test, EggPlant, Test Complete, Coded UI, Selenium, Postman
  • Continuous Deployment: Jenkins, Bamboo, Prism, Microsoft DevOps

2. Cloud platforms and Virtualisation as development and test environments

Today, most software products are built to support multiple platforms, be it operating systems, application servers, databases, or Internet browsers. Software development teams need to test their products in all of these environments in-house prior to releasing them to the market.

This presents the challenge of creating all of these environments as well as maintaining them. These challenges increase in complexity as development and test teams become more geographically distributed. In these circumstances, the use of cloud platforms and virtualisation helps, especially as these platforms have recently been widely adopted in all industries.

Automation on cloud and virtualised platforms enables delivery teams to rapidly spin up/down environments optimising infrastructure utilisation aligned with demand while, similar to maintaining code and configuration version history for our products, also maintain the version history of all supported platforms. Automated cloud platforms and virtualisation introduces flexibility that optimises infrastructure utilisation and the delivery footprint as demand changes – bringing savings across the overall delivery life-cycle.

Example:

When a build and release engineer changes configurations for the target platform – the operating system, database, or application server settings – the whole platform can be built and a snapshot of it created and deployed to the relevant target platforms.

Virtualisation: The virtual machine (VM) is automatically provisioned from the snapshot of base operating system VM, appropriate configurations are deployed and the rest of the platform and application components are automatically deployed.

Cloud: Using a solution provider like Azure or AWS to deliver Infrastructure-as-a-Service (IaaS) and Platform as a Service (PaaS), new configurations can be introduced in a new environment instance, instantiated, and configured as an environment for development, testing, staging or production hosting. This is crucial for flexibility and productivity, as it takes minutes instead of weeks to adapt to configuration changes. With automation, the process becomes repeatable, quick, and streamlines communication across different teams within the Tech-hub.

3. Distributed version control systems

Distributed version control systems (DVCS), for example GIT, Perforce or Mercurial, introduces flexibility for teams to collaborate at the code level. The fundamental design principle behind DVCS is that each user keeps a self-contained repository with complete version history on one’s local computer. There is no need for a privileged master repository, although most teams designate one as a best practice. DVCS allow developers to work offline and commit changes locally.

As developers complete their changes for an assigned story or feature set, they push their changes to the central repository as a release candidate. DVCS offers a fundamentally new way to collaborate, as  developers can commit their changes frequently without disrupting the main codebase or trunk. This becomes useful when teams are exploring new ideas or experimenting as well as enabling rapid team scalability with reduced disruption.

DVCS is a powerful enabler for the team that utilise an agile-feature-based branching strategy. This encourages development teams to continue to work on their features (branches) as they get ready, having fully tested their changes locally, to load them into next release cycle. In this scenario, developers are able to work on and merge their feature branches to a local copy of the repository.After standard reviews and quality checks will the changes then be merged into the main repository.

To conclude

Adopting these three major trends in the delivery life-cycle enables a organisation to imbed proper release management as a strategic competitive advantage. Implementing these best practices will obviously require strategic planning and an investment of time in the early phases of your project or team maturity journey – this will reduce the organisational and change management efforts to get to market quicker.

SPHERE enables Two NHS trusts to deploy Citrix cloud services

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Two London trusts are to deploy cloud-based services which are expected to be used by more than 7,000 staff.

Systems Powering Healthcare (SPHERE), a provider of IT systems and infrastructure to hospitals, is to deploy Citrix Cloud services at Chelsea and Westminster NHS Foundation Trust and The Royal Marsden NHS Foundation Trust.

Chelsea and Westminster is one of NHS England’s global digital exemplars.

The trusts are currently piloting Citrix Workspace Service running on Microsoft Azure, but are expected to deploy fully within three to six months. Once fully live, staff will be able quickly and securely access data and applications from anywhere.

“The NHS healthcare organisations we service are faced with ageing IT infrastructure and little capital funds available to replace it”, Aaron Aldrich, vice president and head of operations at SPHERE told Digital Health News.

“We were tackling an inability to scale up or down rapidly to meet customer demands without significantly over-investing in technology kit.

“The Citrix-Azure cloud combination offers us rapid scalability. It also reduces time and spend on maintaining, producing, installing and configuring on-premise infrastructure.”

Aldrich added that cloud infrastructure will reduce the need for organisations to own and maintain large amounts of IT equipment.

“Requiring fewer devices and apps as well as less infrastructure and real-estate will allow NHS trusts to cut down cost per device and greatly reduce time and money spent on IT.”

In recent years, increasing numbers of healthcare organisations have been investigating the potential benefits of cloud-based software, infrastructure and platforms. Last month, Digital Health News reported that BT and East Sussex were partnering to create dedicated virtual networks.

SPHERE Invests in Elastic Cloud Storage (ECS)

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Systems Powering Healthcare (SPHERE) is investing in the future – our EMC Elastic Cloud Solution, or at least a portion of it, has arrived and is being installed.  This enhances SPHERE’s service catalogue to provide flexible storage service solutions to various systems.

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EMC Elastic Cloud Storage (ECS) is a software-defined object storage platform marketed by EMC Corporation. ECS was designed to adhere to several tenets of object storage, including scalability, data resiliency and to take advantage of existing or new commodity server hardware in order to manage costs. At an architectural level, EMC ECS offers Software-Defined Storage (SDS). This flexibility not only simplifies the deployment of ECS, but it also allows ECS to provide a single pool of data that can spread data across a variety of underlying infrastructure components, including commodity or ECS appliances, and even EMC and third-party storage arrays. In addition to the flexibility offered by the software-based architecture, ECS enables multi-head access, allowing different protocols, such as object and HDFS, to access the same data concurrently.