what must you define to implement a pipeline that reads data from azure blob storage?

To implement a pipeline that reads data from Azure Blob Storage, you must define the following key components:

1. Linked Service

• Define a Linked Service to connect to your Azure Blob Storage account. This includes specifying the storage account name and authentication credentials (such as account key or managed identity)

2. Dataset for the Source

• Define a Dataset that points to the specific container or folder path in Azure Blob Storage where your data resides.
• Specify the file format (e.g., CSV, JSON, Parquet) and optionally file filters or wildcard patterns to select files.
• Configure properties such as recursive folder traversal and file name filters if needed

3. Dataset for the Destination

• Define a Dataset for the destination where the data will be copied or processed (e.g., Azure SQL Database, Azure Data Lake, another Blob container).
• Specify the schema or table structure if applicable

4. Pipeline and Activities

• Create a Pipeline that orchestrates the data movement.
• Add a Copy Activity to the pipeline, specifying:
  • The source dataset with type set to BlobSource.
  • The sink dataset with the appropriate sink type (e.g., SqlSink for Azure SQL Database).
  • Additional copy behavior settings such as preserving folder hierarchy, merging files, or flattening hierarchy if copying multiple files

5. Schema Mapping (Optional but Recommended)

• Define column or field mappings between source and destination datasets to ensure data compatibility and correct transformation during copy

6. Trigger (Optional)

• Define triggers to schedule or automate pipeline runs, such as time-based schedules or event-based triggers (e.g., file arrival in Blob Storage)

Summary

You must define:

• A linked service to connect to Azure Blob Storage.
• A source dataset specifying the blob container, path, and file format.
• A destination dataset for the target storage.
• A pipeline with a copy activity referencing these datasets.
• Optionally, schema mappings and triggers for automation.

This setup ensures your pipeline can read data from Azure Blob Storage and process or move it as required