Migration of Informatica to Azure Data Factory and PL/SQL to Synapse SQL

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A provincial transportation ministry needed to migrate its existing data integration from Informatica to Azure Data Factory (ADF) and PL/SQL code to Synapse SQL to be able to leverage the advanced capabilities of the Azure ecosystem to enhance data integration, workflow orchestration and overall data management.

Client Challenges and Requirements

  • 844 Mappings and 45 workflows
  • 111 PL/SQL objects
  • Limited knowledge / documentation of legacy system
  • Thoughtful redesign of Orchestration features and functionality in ADF
  • Stringent timeline of 22 weeks
  • Redesign of frameworks which were tightly coupled with Informatica
  • Limited Azure awareness with internal stakeholders
  • Unavailability of adequate test data due to compliance issue

Bitwise Solution

Bitwise provided ETL migration services using its proprietary automated ETL Conversion solution to accelerate migration timeline within budget requirements.

 

  • Detailed analysis of legacy system with assessment report.
  • Customized solution design created per client’s business needs.
  • Recommended and implemented Microsoft best practices throughout.
  • Leveraged Bitwise automated Data Validation utility to efficiently compare source and target tables / files (row by row and column by column).
  • Prepared and shared comparison report and test case document with client for future reference.
  • Calibrated ADF pipeline performance to meet business needs.
  • Completed knowledge transfer of new ADF code to client stakeholders.
  • Created comprehensive documentation for solution build and operational support.
  • Provided warranty support to ensure smooth deployment.

Tools & Technologies We Used

Bitwise ETL Converter
Informatica PowerCenter
PL/SQL
Azure Data Factory (ADF)
Azure Synapse SQL
Bitwise Data Validation Utility

Key Results

Successful on-time delivery of converted jobs in ADF and Synapse SQL within strict 22-week timeline

Performance and cost optimization achieved by creating 11 re-usable pipelines and 6 re-usable flowlets

Ability to leverage the advanced capabilities of Azure Data & AI ecosystem

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