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Databricks on Azure (Data Pipelines)

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Case Study: Automated Data Pipelines Using Databricks on the Azure Platform

 

Project Goal

 

The project aimed to replace outdated and expensive legacy data flows with modern pipelines built on the Databricks platform. The new solution was designed to deliver:

  • lower operational costs,

  • improved data quality,

  • faster data delivery,

  • higher refresh frequency,

  • full data test automation,

  • alignment with the client’s architecture and internal tools.

The project was executed by a QAbird team consisting of a Lead Data Engineer and two QA Engineers specializing in Databricks testing.

Technical Scope & Activities

Building and Testing Data Pipelines

 

  • Delivered more than initially planned:

    • Planned: 17 pipelines

    • Delivered: 21 pipelines – all on schedule

  • Manual testing of Databricks workflows:

    • Validating process correctness and data integrity

    • Detailed data comparisons with the legacy system

    • Validation against the target data model

Test Automation

 

  • Automated validation of the data model (Source to Target Mapping)

  • Data profiling integration – early detection of anomalies

  • Integrated into CI/CD – fully automated data quality testing in deployment pipelines

Documentation and Transparency

 

  • Detailed test plan and test case suite (Excel + Azure DevOps)

  • STTM file verification

  • Process and validation documentation in Confluence – ensuring full knowledge transfer to the client’s teams

Flexible Approach & Client Optimization

 

The project was executed with full adaptation to the client’s internal tools, ensuring that:

  • ongoing system maintenance by the client's operational team would be simplified,

  • consistency with the organization’s IT architecture was preserved.

Scale and Technical Challenges

 

  • One of the largest pipelines processed over 20 million records

  • Maintaining the correct refresh order across 21 flows was critical, especially due to surrogate key (SKID) dependencies

  • Careful selection of tools to ensure seamless integration with other client systems

Timeline and Team

 

  • Project start: November 2

  • Go-live: June of the following year

  • Duration: 8 months


QAbird team:


  • 1 x Lead Data Engineer

  • 2 x QA Engineers (Databricks testing specialists)

Technologies Used

 

  • Cloud platform: Microsoft Azure

  • Data processing: Databricks, Python, PySpark, SQL

  • Version control: GIT

  • Testing & documentation: Excel, Azure DevOps, Confluence

Results & Business Value

 

  • Reduced operational costs by eliminating legacy tools

  • Increased data refresh frequency

  • Fully automated testing and documentation

  • Improved reliability and data quality across the organization

  • Project delivered above scope and fully on time

From the very beginning, we monitored project quality using our proprietary Satisfaction Survey system – regular satisfaction checks allowed for fast response to the client’s needs and continuously confirmed the value of the delivered solution.