Reimagining Data Engineering with Databricks Genie-Powered Agentic ETL

Reimagining Data Engineering with Databricks Genie-Powered Agentic ETL - Case Study Thumbnail-524×350 px
Solution Agentic ETL Using Genie Skills
Solution Agentic ETL Using Genie Skills
Industry CPG
Region Global
Technology Databricks
Context
As data environments expand across functions and source systems, manual pipeline development can constrain engineering capacity, extend delivery cycles, and create inconsistencies in implementation. Enterprises need to increase development velocity without compromising engineering standards or human accountability. An AI-assisted approach can help automate repeatable work while enabling engineers to focus on validation, review, and decisions requiring business context across the enterprise.
Problem Statement

The enterprise relied heavily on manual pipeline development, consuming engineering capacity and slowing delivery as requirements increased across data domains. Repetitive implementation also created opportunities for inconsistent code quality and standards adherence. Limited use of AI constrained automation, while complex business logic and data-specific decisions continued to require experienced human review. 

Impact

  • ~70% productivity gain through AI-assisted pipeline development, reducing delivery time for 22 pipelines from five weeks to two. 
  • 7 reusable Genie skills established to standardize repeatable data engineering workflows across source systems and future use cases. 
  • Higher-value engineering capacity enabled by shifting teams from manual pipeline authoring to review, validation, and decision-making. 
  • Improved code quality and consistency through DDF-aligned standards embedded into AI-generated pipeline development. 
  • $4–$7 cost per pipeline run achieved based on pipeline complexity and the number of iterations required. 

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