Migrating data warehouse ETL projects from PL/SQL to Azure Data Factory (ADF)

Part1: Azure Data Factory-An In-Depth Introduction with Practical Scenarios and Exercises | LinkedIn

Migrating data warehouse ETL projects from PL/SQL to Azure Data Factory (ADF) involves several key steps:

Assessment:

Evaluate the existing PL/SQL ETL processes to understand their functionality, dependencies, and performance requirements. This helps in planning the migration and identifying any potential challenges.

Design:

Design the new ETL processes in ADF. This includes mapping out data flows, transformations, and the overall architecture of the data pipeline.

ADF provides tools like Mapping Data Flows for designing transformations and Data Flows for orchestrating the data pipeline.

Conversion: Convert the PL/SQL code to ADF components. This can be done manually or using automated tools that help translate PL/SQL scripts to ADF activities.

For example, Bitwise offers a proprietary ETL Converter solution that can automate this process.

Testing: Thoroughly test the new ADF pipelines to ensure they perform as expected. This includes unit testing, integration testing, and performance testing to validate the accuracy and efficiency of the migrated ETL processes.

Deployment: Deploy the ADF pipelines into production. This involves setting up the necessary Azure resources, configuring the pipelines, and ensuring they are scheduled correctly to run as needed.

Monitoring and Optimization: Once the ADF pipelines are running, monitor their performance and optimize as necessary. ADF provides monitoring and alerting capabilities to help manage and troubleshoot the pipelines.

By following these steps, you can effectively migrate your data warehouse ETL projects from PL/SQL to Azure Data Factory, leveraging the scalability, cost-effectiveness, and integration capabilities of ADF.