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3.1.4. Design for Data Integration

💡 First Principle: Orchestrating the movement and transformation of data from diverse sources into a unified, prepared dataset is the essential foundation for enabling scalable analytics, reporting, and machine learning.

Scenario: You are designing a data warehouse solution that requires aggregating data from various sources: on-premises SQL Server databases, Azure Blob Storage, and a SaaS CRM application. This data needs to be transformed (cleaned, enriched) and loaded into Azure Synapse Analytics daily.

Data integration refers to the process of combining data from various sources to provide a unified view. In the context of Azure, this often involves Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) operations.

Azure’s core data integration services:

  • Azure Data Factory (ADF): A cloud-based service for orchestrating, moving, and transforming data across diverse sources, enabling scalable ETL/ELT workflows.
  • Azure Synapse Analytics: Unifies enterprise data warehousing and big data analytics, providing a single, scalable platform for end-to-end analytics. It includes built-in data integration capabilities.
Key design considerations for Data Integration:
  • Data Movement: Connectors for various sources, hybrid connectivity for on-premises data.
  • Data Transformation: Code-free (e.g., Data Flows in ADF) or code-based (e.g., Spark in Synapse).
  • Orchestration: Scheduling, monitoring, and managing complex data pipelines.
  • Scalability & Reliability: Ensuring pipelines can handle growing data volumes and are resilient to failures.

⚠️ Common Pitfall: Writing custom code for complex data integration tasks that could be handled more efficiently and reliably by a managed service like Azure Data Factory. This increases development time and operational burden.

Key Trade-Offs:
  • ETL vs. ELT: In ETL, data is transformed before loading into the target system. In ELT, raw data is loaded first, and transformations are performed within the target system (e.g., a data warehouse like Synapse). ELT often leverages the power of the target system for transformations.

Reflection Question: How do Azure's data integration services (Azure Data Factory for orchestration and movement, Azure Synapse Analytics for analytics and transformation) collectively enable you to design scalable ETL/ELT workflows, moving and transforming data across diverse sources for unified analytics and reporting?