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SAS to Big Data Migration Platform

A strategic migration from SAS to a Dataiku, Hadoop and Spark platform with CI/CD across environments.

Application sectors

  • Banking
  • Credit Risk
  • Data Platform

Technologies

  • Dataiku
  • PySpark
  • Hadoop
  • Hive
  • CI/CD
Abstract data infrastructure and network visualization

Business issue

Why I was brought into the project

The credit risk platform needed to migrate critical SAS pipelines to a modern Big Data stack while preserving production reliability and governance.

Context

The environment around the project

A credit risk team needed to migrate SAS pipelines to a Big Data platform.

Functional environment

The functional context covered credit risk data pipelines, governance, documentation and production delivery constraints.

Technical environment

The technical environment combined Dataiku, Python, PySpark, Hadoop, Hive, Big Data, SQL, SAS, Confluence and Agile practices.

Challenges

The migration involved critical pipelines, multiple execution environments, HDFS data design, CI/CD, user support and cross-functional coordination.

Solution

My contribution and its impact

My contribution to the project

I contributed to the migration plan, designed target HDFS structures, implemented optimized Dataiku and PySpark pipelines, and set up CI/CD across environments.

  • Migration plan from SAS to Dataiku and Hadoop
  • Optimized PySpark and Dataiku pipelines
  • CI/CD process across development, staging and production
  • Platform services for versioning, governance and documentation

Impact

The project helped move critical risk processing toward a more maintainable and industrialized platform while supporting users and enforcing development standards.

  • Modernized credit risk pipeline execution
  • Improved development and deployment practices
  • Better governance and user support on the new platform

Impact metrics

  • Multi-environment CI/CD
  • SAS migration
  • Big Data pipelines

Approach

How I structured the work

  1. Analyze SAS architecture and define the target Big Data and Dataiku structure.
  2. Develop optimized PySpark and Dataiku pipelines with industrialized scenarios.
  3. Set up CI/CD, documentation and user support practices across environments.

Takeaways

What I learned from this project

  1. Large platform migrations need governance, training and support in addition to pipeline development.
  2. CI/CD becomes a central reliability lever when multiple environments are involved.
  3. Modernizing legacy SAS assets requires understanding both old architecture and new platform constraints.