Data Engineering Case Studies

Real projects showcasing Data-bricks vs Snowflake performance, cloud migration outcomes, streaming solutions, and analytics modernisation.

CASE STUDY 1 — Migrating Legacy ETL Workloads from On-Prem SQL Server to Databricks Lakehouse

Executive Summary

A utilities client was struggling with long-running daily ETL jobs built on legacy SSIS/SQL stored procedures. Daily SLA breaches were common due to compute limits on an on-prem SQL Server cluster. Datamethods migrated the entire ETL framework to Databricks using a Bronze → Silver → Gold medallion architecture.

Problem Statement

On-prem SQL Server became a performance bottleneck.
ETL runs took 12–14 hours, failing the morning reporting SLA.
Storage limits prevented landing large raw files.
Company wanted incremental loads, not full refreshes.
No lineage, no monitoring, no reliability.

Solution

Built a Databricks Lakehouse using ADLS.
Recreated all SSIS logic using PySpark + Delta Live Tables.
Designed a fully automated Bronze → Silver → Gold pipeline.
Implemented orchestration using Azure Data Factory.
Added data quality rules using Delta constraints & checkpoints.

Performance Gains

Business Value

Delivered 85% performance improvement.
Reduced operational cost by 40%.
Allowed analytics teams to consume data before 6 AM every day.

CASE STUDY 2 — Snowflake vs Databricks Performance Comparison for High-Volume Finance Analytics

Client

Large financial services company running daily credit-risk simulations.

Objective

Compare Snowflake and Databricks compute performance for:

800M row credit exposure fact table
Complex window functions
Joins between 6 large tables
30-day refresh cycle

Experiment Setup

Results

Conclusion

Databricks performed 35–45% faster for compute-heavy workloads.
Snowflake was more cost-effective for lighter analytical queries.
For ML-driven workloads, client moved to Databricks Lakehouse.