Learn step-by-step how to create Azure Data Factory pipelines for extracting, transforming, and loading data. These pipelines enable seamless data integration and processing in the cloud.
As someone who has worked with various ETL tools azure like SSIS and Informatica, I was initially intimidated by Azure Data Factory.
But after creating my first few pipelines, I realized it’s quite easy to build effective ETL solutions on ADF.
In this guide, I’ll walk you through the key steps I follow when developing Azure Data Factory pipelines for ETL:
Connect to Data Sources
The first step is connecting to your data sources. ADF supports a wide variety of data stores like Azure SQL Database, Azure Blob Storage, AWS S3, etc.
You can easily link these sources to your ADF by creating Linked Services. For example, to connect to Azure SQL, you would create an “Azure SQL Database Linked Service” and provide your server name and database name.
CreateDatasets
After connecting to sources, you need to create Datasets that represent the structure of your source data. For example, you may create an Azure SQL Dataset with the schema of your Customer table.
Datasets are important because they allow ADF to understand your data types and format without having to manually specify them later.
Design the ETL Flow
This is where you design the actual ETL process by adding activities like Copy Data, Data Flow, Stored Procedure, etc.
For example, you may:
- Copy from Blob Storage to Azure SQL Database using the Copy Data activity
- Cleanse the data using a Data Flow activity
- Call a Stored Procedure to insert the rows into a table
ADF has a drag-and-drop interface that makes pipeline design intuitive.
Debug and Publish
Once your pipeline is ready, you can debug it using parameter overrides. ADF allows you to execute pipelines manually with different parameter values.
After thorough testing, you publish your pipeline and then configure triggers to run it on a schedule or trigger it based on events.
Monitor and Manage
Once published, you can monitor the pipeline runs in the portal. You can track metrics like run time, rows copied, errors, etc.
ADF integrates with Alerts to notify you in case of failures. You can also track lineage between data assets for auditing.
Key Takeaways
- ADF makes it easy to build scalable ETL processes with minimal coding
- Link services connect to a vast array of data sources
- Datasets and pipeline activities enable graphical pipeline construction
- Publishing and triggers make the solution production-ready
- Rich monitoring provides insights into data flows
By following these steps, you can build robust Azure Data Factory pipelines to efficiently move data between sources, transform it, and load it into destinations. ADF brings enterprise-grade ETL capabilities to the cloud.
