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Global Corp Finance

Case Study

Problem Statement

Global Corp’s core finance pipeline was joining tens of millions of AR transactions with a Customer Master table containing only thousands of records, yet the process took over 2 hours daily. This caused three major business failures:

  • Operational inefficiency: The finance team could not start their day on time.

  • Data unreliability: Corrupted data was being delivered to senior leadership.

  • Technical bottleneck: The data engineering team lacked visibility into why the job was slow and how to prevent recurring failures.

The organization had no recovery mechanism for bad data and no performance tuning strategy in place

Solution

A comprehensive optimization and reliability engineering approach was implemented across the finance data pipeline:

1. Performance Engineering (PySpark)

  • Pipeline bottlenecks were diagnosed through the Spark UI, where shuffle-heavy stages were identified as the primary performance issue.

  • Inefficient joins were replaced with a Broadcast Join to optimize large-to-small table operations.

  • Execution consistency and schema correctness were ensured through enforced schema validation.

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2. Data Reliability (Delta Lake)

  • Schema Enforcement was enabled to prevent corrupted or invalid records from entering the dataset.

  • The Delta Lake Transaction Log was leveraged using DESCRIBE HISTORY, Time Travel queries, and the RESTORE command to recover from historical data corruption and re-establish trust in reporting outputs.

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3. Table Performance & Maintenance

  • OPTIMIZE was used to address the small file problem through bin-packing.

  • Z-ORDER was applied to improve query performance on key columns.

  • VACUUM was implemented to manage storage growth and maintain long-term efficiency.

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4. Production-Grade SCD Type 2 Pipeline

  • A fully idempotent MERGE-driven SCD Type 2 pipeline was developed to maintain customer history while avoiding duplication.

  • Built-in data quality checks, including Time Travel–based volume validation and automated maintenance steps, were integrated into daily runs.

Business Impact

  • Pipeline runtime was reduced from over two hours to minutes, enabling timely availability of financial reporting data.

  • Historical data corruption was fully remediated through Delta Lake Time Travel and RESTORE functionality.

  • Data trust was re-established for senior leadership by ensuring accuracy, consistency, and recoverability of daily AR Aging outputs.

  • Long-term scalability was achieved through SCD Type 2 automation, optimized storage management, and improved table performance.

  • Operational efficiency increased, allowing finance teams to begin their workflow without delays.

  • Cloud storage and compute costs were reduced through optimized file management and maintenance operations.

PySpark Optimization:

SCD Type 2:

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