Skip to content

🌐 Introduction to Azure Data Factory (ADF)🔗

🔹 What is ADF?🔗

Azure Data Factory is Microsoft’s cloud-based ETL & data integration service. Think of it as a factory for moving and transforming data across different systems, both on-premises and in the cloud.

It’s a serverless service (you don’t manage servers), and it allows you to build data pipelines that automate data movement, ingestion, and transformation.

🔹 Why ADF?🔗

  • Companies often have data scattered across:

  • Databases (SQL, Oracle, PostgreSQL, MongoDB, etc.)

  • Files (CSV, JSON, Parquet in blob storage, data lake, S3, etc.)
  • SaaS apps (Salesforce, SAP, Dynamics, etc.)
  • ADF connects these sources, moves data, and transforms it into a structured form for reporting, analytics, or AI/ML.

🔹 Core Concepts🔗

  1. Pipelines

  2. A pipeline = workflow that defines a series of activities (like copying, transforming, loading).

  3. Example: Extract data from SQL → Transform in Databricks → Load into Synapse.

  4. Activities

  5. Steps inside a pipeline.

  6. Types:

    • Data movement: Copy data from source to sink.
    • Data transformation: Run Databricks notebooks, Spark jobs, SQL scripts.
    • Control: Loops, conditions, wait, execute another pipeline.

  7. Datasets

  8. Represent the data structure (like a table, a file path, or a folder).

  9. Example: A dataset could point to a CSV file in Azure Blob Storage.

  10. Linked Services

  11. Connection information (credentials, endpoints).

  12. Example: Linked service for Azure SQL DB, one for Data Lake.

  13. Integration Runtime (IR)

  14. The compute infrastructure ADF uses to move/transform data.

  15. Types:

    • Azure IR: Fully managed in the cloud (default).
    • Self-hosted IR: For connecting on-prem systems.
    • SSIS IR: For running SSIS packages.

🔹 Common Use Cases🔗

  • ETL / ELT pipelines Ingest raw data → transform into clean data → load into data warehouse (like Synapse or Snowflake).
  • Data Lake Ingestion Collect logs/files into Azure Data Lake Gen2.
  • Hybrid Data Movement Move data from on-prem SQL Server to Azure Synapse.
  • Big Data Integration Orchestrate Databricks notebooks, Spark, or HDInsight.
  • Scheduling & Monitoring Automate jobs, monitor them with logs and alerts.

🔹 Example Workflow🔗

  1. Copy sales data from on-prem SQL Server into Azure Data Lake daily.
  2. Trigger a Databricks notebook to clean and enrich the data.
  3. Load processed data into Azure Synapse Analytics.
  4. Business analysts connect Power BI → create dashboards.

🔹 Benefits🔗

  • Serverless → no infra to manage.
  • Scalable → works for small files or terabytes.
  • Cost-effective → pay-per-use.
  • Rich connectors → 100+ sources (DBs, files, APIs).
  • Visual & code-based → drag-and-drop UI + JSON definitions.
  • Monitoring → built-in logging, retry, alerts.

👉 In short: ADF = a data pipeline orchestration tool in Azure. It moves, transforms, and organizes data so that downstream systems (like Synapse, Databricks, Power BI) can use it. Do you want me to go next into ADF architecture (with diagram) or step-by-step how to build your first pipeline?