ADF Overview

ADF Overview

• ADF is a serverless, fully managed service for data ingestion, basic transformations, and orchestration.
• Suitable for:
  • Transferring data between various sources and sinks (e.g., RDBMS ↔ ADLS Gen2).
  • Performing basic transformations using Dataflows.
  • Orchestrating pipelines for data processing.

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Use-Case 1: Ingest Data from RDBMS to ADLS Gen2

Steps:

1. Pre-Steps:
   • Create a Resource Group and organize resources (e.g., Azure SQL Database and ADLS Gen2).
   • Set up Azure SQL Database as the source and create/insert data into a table.
   • Set up ADLS Gen2 Storage Account as the sink (enable hierarchical namespace, create container and directory).
2. Data Factory Setup:
   • Create ADF Resource and connect to the source (Azure SQL) and sink (ADLS Gen2) via Linked Services.
   • Define datasets for source and sink (specify formats and paths).
   • Create a pipeline with a Copy Activity to ingest data.
   • Debug and monitor pipeline execution.
   • For transformations, use Mapping Dataflows (e.g., SELECT and AGGREGATE transformations).

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Use-Case 2: Ingest Data from External URL to ADLS Gen2

Steps:

1. Create a Resource Group, Storage Account, and ADF Resource.
2. Use Linked Services:
   • Source: HTTP connector for external URL (e.g., orders.csv).
   • Sink: ADLS Gen2 connector.
3. Define datasets for source (CSV format) and sink.
4. Create a pipeline:
   • Add a Copy Activity to transfer data.
   • Perform basic transformations in Dataflow:
     • Remove order_date column and rename order_customer_id to customer_id (using SELECT transformation).
     • Calculate the count of each order status (using AGGREGATE transformation).
   • Debug and publish the pipeline.

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Use-Case 3: Automate Retail Data Pipeline (Blob → ADLS Gen2)

Steps:

1. Set up Blob Storage as the source and upload products.csv.
2. Create ADLS Gen2 as the sink.
3. Configure ADF:
   • Add Linked Services for source (Blob) and sink (ADLS Gen2).
   • Define datasets for the source and target files.
4. Create a pipeline to copy data from Blob to ADLS Gen2.

Enhancements:

1. File Automation:
   • Use the Validation Activity to automatically pick files from Blob as they arrive.
2. Sanity Checks:
   • Use Get Metadata Activity to validate file size, column count, etc.
   • Use If Condition Activity to enforce validations dynamically.
3. Failure Notifications:
   • Use the Fail Activity and Alert Rule to notify on pipeline execution failure.
4. Schedule Pipelines:
   • Use a Trigger to schedule pipeline execution.

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Limitations of ADF

• No storage: ADF does not store data itself.
• Basic transformations only: Complex transformations require external tools like Databricks or HDInsight.
• No streaming capabilities: Not suitable for real-time data ingestion.
• Not a migration tool: Designed for ETL workflows, not full-scale migrations.

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This structure simplifies the process while highlighting key steps and enhancements for production-ready pipelines.