Analytical pipeline modernisation

I worked in a financial-data environment on a recurring analytical calculation that depended on a legacy SQL Server process. My role covered understanding the existing logic, migrating the workflow to Databricks and PySpark, adding quality and validation controls, and preparing the handover.

Legacy analytical processes often grow as a mix of SQL, stored procedures and implicit business rules. When the logic is hard to isolate, small changes become risky, validation is slow and teams hesitate to modify or scale the process. That is the kind of situation I worked in: a recurring calculation that was difficult to maintain, adapt and operate with confidence.

• Map the legacy calculation logic, dependencies and expected outputs.
• Rebuild the workflow on Databricks with PySpark.
• Introduce reusable, parameterised transformation components.
• Add automated data-quality controls and statistical validation.
• Reconcile migrated outputs against the legacy process.
• Prepare technical documentation and handover material for the teams who would operate the process.

• Business rules had to remain traceable while the implementation changed.
• Differences between legacy and migrated outputs needed explicit reconciliation.

1. 01

   Study the existing logic

   I started by documenting calculation rules, dependencies, expected outputs and acceptance criteria. Before rewriting anything, I needed a clear picture of what the legacy process was supposed to produce.

2. 02

   Migrate onto Databricks and PySpark

   I reimplemented the workflow as PySpark transformations running on Databricks, so the process could be developed, tested and operated as a modular data pipeline rather than a brittle legacy calculation.

3. 03

   Make the workflow modular and parameterised

   I broke the logic into reusable components with explicit parameters. That made the process easier to understand, adjust and reuse without rewriting the whole calculation each time a requirement changed.

4. 04

   Add quality, validation and reconciliation

   I introduced automated data-quality checks, statistical validation and output reconciliation against the legacy workflow. The goal was to make differences visible and discussable, not to hide them.

5. 05

   Document and hand over

   I prepared documentation covering execution requirements, known limitations and integration considerations, so the people operating the process could understand how to run it and where to be careful.

• A migrated Databricks and PySpark workflow.
• Reusable and parameterised transformation components.
• Automated data-quality controls.
• Statistical validation and reconciliation checks.
• Technical documentation and handover material.

• PySpark
• Databricks
• SQL Server
• SQL
• Data quality
• Statistical validation

• Professional experience

  Delivery scope

  Databricks and PySpark migration with quality and validation controls

  This case records the scope, responsibilities and deliverables of my work.

• This case does not provide a reproducible copy of the original environment.
• I do not claim quantified performance gains or measured business impact.

• How orchestration, retries and failure recovery would work in the target environment.
• Whether data contracts and schema-change handling are explicit enough.
• Access control and sensitive-data governance around the process.
• Monitoring, lineage and operational alerting.
• Compute-cost and performance tuning under realistic workloads.
• Reconciliation, rollback and a controlled migration path from the legacy process.

• SQL or SAS analytical-process migration to PySpark.
• Databricks pipeline and workflow modernisation.
• ETL and data-quality design.
• Analytical-process automation.
• Technical assessment and migration roadmap.
• Delivery documentation and handover.

• View the public repository

  Data Quality Pipeline Lab is an independent public implementation. It uses synthetic data, synthetic rules and a synthetic architecture. It does not reproduce employer code, data or systems. It demonstrates contracts, quarantine, reconciliation, idempotency, tests and CI.