Introduction to Azure Synapse Pipelines | Data Integration & ETL in Synapse Analytics | Azure Synapse Analytics Tutorial

Introduction to Azure Synapse Pipelines | Data Integration & ETL

Introduction to Azure Synapse Pipelines | Data Integration & ETL in Synapse Analytics

📘 Overview

Azure Synapse Pipelines provide a unified platform for building and orchestrating data integration workflows in the Azure Synapse workspace. With Synapse Pipelines, you can move, transform, and process data at scale, similar to Azure Data Factory, but tightly integrated with Synapse Analytics.

🚀 Key Features

  • Low-code drag-and-drop pipeline designer
  • Support for multiple data sources including Azure Data Lake, SQL, Blob, Cosmos DB, etc.
  • Activities for data movement, transformation (Data Flows), notebooks, and stored procedures
  • Trigger-based orchestration (manual, schedule, tumbling window, event)

🛠️ Components of a Synapse Pipeline

  • Pipeline: A logical container of activities
  • Activity: A task like copy, execute notebook, data flow, or web call
  • Dataset: Defines the structure and location of the data
  • Linked Service: Connection information to data stores or compute
  • Trigger: Defines how and when a pipeline should run

📥 Sample Pipeline Flow

Here's a basic flow of a pipeline in Synapse:

  1. Trigger fires a pipeline on schedule
  2. Copy activity moves data from Blob Storage to SQL Pool
  3. Notebook activity cleans or transforms the data
  4. Stored procedure updates metadata or logs

📊 Example: Create a Pipeline with Copy Activity

1. Go to Synapse Studio → Integrate tab
2. Click "+ New pipeline"
3. Add "Copy Data" activity
4. Configure source (e.g., CSV file in ADLS Gen2)
5. Configure sink (e.g., table in SQL pool)
6. Publish and trigger manually or via schedule

📌 Best Practices

  • Use parameterization to create reusable pipelines
  • Monitor with Synapse Monitoring or Log Analytics
  • Break complex flows into modular pipelines

🎯 Use Cases

  • Batch ingestion and transformation of files
  • Orchestration of Spark notebooks with SQL pipelines
  • Building data warehouses and lakes

📺 Watch the Full Video Tutorial

📚 Credit: Content created with the help of ChatGPT and Gemini.