Here’s a comprehensive overview of data design patterns, their use cases, and the corresponding Microsoft technologies, frameworks, and architectures to implement them:
1. Batch Data Processing Pattern
• Use Cases:
• Periodic ETL (Extract, Transform, Load) jobs for large datasets.
• Data warehouse population.
• Generating aggregated reports.
• Architectural Components:
• Data Storage: Azure Data Lake, Azure Blob Storage.
• Processing Framework: Azure Data Factory, Azure Synapse Analytics.
• Orchestration: Azure Data Factory pipelines, Logic Apps.
• Key Tools: Azure SQL Database, Databricks for transformations.
2. Streaming Data Processing Pattern
• Use Cases:
• Real-time analytics for IoT devices.
• Fraud detection in financial transactions.
• Monitoring website clickstreams.
• Architectural Components:
• Data Ingestion: Azure Event Hubs, Azure IoT Hub.
• Processing Framework: Azure Stream Analytics, Azure Databricks.
• Data Storage: Azure Cosmos DB, Azure Data Explorer, Azure SQL.
• Visualization: Power BI, Azure Monitor.
• Key Tools: Stream Analytics query language, Apache Spark.
3. Lambda Architecture
• Use Cases:
• Combining batch and real-time data for unified analytics.
• Handling large-scale systems requiring fault tolerance and scalability.
• Architectural Components:
• Batch Layer: Azure Synapse Analytics, Azure Data Factory.
• Speed Layer: Azure Stream Analytics, Azure Event Hubs.
• Serving Layer: Azure Cosmos DB, Azure SQL Database.
• Data Visualization: Power BI.
• Key Tools: Azure Synapse Pipelines, Spark SQL.
4. Micro-batch Processing Pattern
• Use Cases:
• Near real-time processing when full streaming isn’t feasible.
• Scenarios with predictable workloads, e.g., financial data aggregation.
• Architectural Components:
• Ingestion: Azure Event Hubs, Azure Data Factory.
• Processing: Azure Databricks (structured streaming).
• Storage: Azure Data Lake, Azure SQL.
• Key Tools: Structured Streaming, PySpark.
5. Data Lake Pattern
• Use Cases:
• Centralized repository for structured and unstructured data.
• Big data analytics and machine learning.
• Architectural Components:
• Storage: Azure Data Lake Storage Gen2.
• Processing: Azure Synapse Analytics, Databricks, HDInsight.
• Cataloging: Azure Purview for metadata management.
• Access: Azure AD for authentication and authorization.
• Key Tools: Delta Lake, Hive Metastore.
6. Data Warehouse Pattern
• Use Cases:
• Business intelligence (BI) and reporting.
• Historical data storage for analysis.
• Architectural Components:
• Data Warehouse: Azure Synapse Analytics.
• Ingestion: Azure Data Factory, SQL Managed Instance.
• Visualization: Power BI.
• Key Tools: T-SQL, PolyBase.
7. Event-Driven Data Processing Pattern
• Use Cases:
• Triggered processing, such as updating a database after receiving an event.
• Log monitoring and alerting systems.
• Architectural Components:
• Ingestion: Azure Event Grid, Azure Event Hubs.
• Processing: Azure Functions, Azure Stream Analytics.
• Storage: Azure Cosmos DB, Azure SQL Database.
• Key Tools: Logic Apps, Event Grid subscribers.
8. Data Mesh Pattern
• Use Cases:
• Decentralized data architecture for large-scale organizations.
• Data products managed by domain-specific teams.
• Architectural Components:
• Domain Data Ownership: Separate Azure Data Lake instances.
• Processing Framework: Azure Synapse Analytics, Databricks.
• Metadata Management: Azure Purview.
• Key Tools: APIs for data interoperability, Data Sharing via Azure Data Share.
9. Machine Learning Data Preparation Pattern
• Use Cases:
• Training machine learning models.
• Feature engineering and data preparation pipelines.
• Architectural Components:
• Data Storage: Azure Data Lake, Azure Blob Storage.
• Processing Framework: Azure Databricks, Azure ML Pipelines.
• Model Training: Azure Machine Learning Studio.
• Key Tools: Python (PySpark, Pandas), MLflow for tracking.
10. Data Governance and Lineage Pattern
• Use Cases:
• Ensuring compliance with regulatory standards (e.g., GDPR, HIPAA).
• Data quality and lineage tracking.
• Architectural Components:
• Cataloging: Azure Purview for metadata and lineage.
• Policies: Azure Policy for data governance enforcement.
• Security: Azure AD, Azure Key Vault for access control.
• Key Tools: Power BI for data audits, Purview Insights.
11. Data Virtualization Pattern
• Use Cases:
• Integrating data across disparate systems without moving it.
• Quick prototyping of analytics solutions.
• Architectural Components:
• Virtualization: Azure Synapse Analytics (on-demand queries).
• Integration: Azure Logic Apps, Data Factory.
• Visualization: Power BI with direct query.
• Key Tools: PolyBase, Linked Servers.
12. Hybrid Cloud Data Architecture
• Use Cases:
• Combining on-premises and cloud data for seamless operations.
• Gradual migration to the cloud.
• Architectural Components:
• On-Premise Gateway: Azure Hybrid Connections, ExpressRoute.
• Cloud Services: Azure Data Lake, Synapse Analytics.
• Integration: Azure Data Factory.
• Key Tools: SQL Server on-premises with replication to Azure SQL.
This framework covers most of the modern data engineering patterns, leveraging the Microsoft technology stack for end-to-end solutions. Let me know if you’d like a deeper dive into any specific pattern or technology!