Case Study: Enterprise Data Landscape Scanner & Lineage Engine
A cloud-agnostic metadata management and data lineage framework supporting 40+ enterprise data technologies, automating end-to-end data discovery, transformation tracking, and regulatory governance.
The Problem
Enterprise organizations lack central visibility into their distributed data assets across fragmented cloud and on-premise ecosystems. This opacity breaks data lineage tracking, increases vulnerability to compliance penalties, and forces reliance on expensive third-party tools to map data dependencies.
Engineering Features & Direct Impact
Serverless Query & ADF Lineage Parser: Extracts pipeline metadata from complex, parameterized Azure Data Factory instances alongside Azure Synapse serverless SQL queries to map dynamic relationships and end-to-end data journeys.
Unified Snowflake Cataloging Connector: Integrates streams, security tagging, and relationship cataloging directly into a single Snowflake scanner, eliminating customer reliance on external lineage-tracking utilities.
Cloud-Agnostic Databricks Scanner: Designs modular, secure Databricks metadata extractors that maintain cross-cloud compatibility across both AWS and Azure environments.
External Lineage Cross-Cataloger: Leads the core integration layer for dbt, Databricks, and Azure Synapse, standardizing distinct metadata schemas into a unified, normalized governance format.
Optimized Memory ETL Pipelines: Restructures embedded H2 database query indexes and refactors Groovy/Python scripts, slashing operational memory consumption while accelerating scanner execution speeds by 10% to 20%.
System-Wide Vulnerability Hardening: Identifies and remediates critical SQL injection exploits across 25+ distinct data platform scanners to achieve a 99% system protection rate.
High-Availability GitOps Migration: Migrates legacy Jenkins pipelines over to automated GitLab CI/CD workflows, cutting developer maintenance overhead while guaranteeing 24/7 scanning infrastructure availability.
The Bottom Line
The System: Automated data discovery across a plug-and-play matrix of 40+ modern enterprise technologies, incorporating automated vulnerability patching and optimized stream processing.
The Business: Minimized client churn by accelerating product delivery timelines, reducing data management costs, and mapping complete, compliance-ready impact analysis maps.
Technical Stack
Java11
Akka Stream
Apache Kafka
Databricks
Python and Groovy