AI-Powered Hive-to-Iceberg Migration Engine
HiveBridge
This project centered on a Java 21 orchestration engine I built to modernize legacy Hive assets at scale. The engine ingested Hive SQL and metadata, analyzed dependencies, and used Azure AI Foundry as an AI-powered transformation component to generate Iceberg-compatible Spark SQL while extracting automated table lineage for downstream migration readiness.
$2M+
Vendor savings
Java 21
Core engine
Automated
Lineage extraction
Iceberg
Target output
The Story
Why it mattered
Legacy Hive workloads were expensive to maintain and hard to modernize without large manual efforts. Teams needed an approach that could translate legacy SQL patterns into Iceberg-compatible Spark SQL while also preserving table lineage and dependency context for safe migration.
How I approached it
I built the Java 21 conversion engine as the center of the migration workflow. It ingested legacy Hive metadata and SQL, established migration context, and orchestrated the transformation process with Azure AI Foundry helping convert Hive logic into modern Spark SQL patterns. A parallel lineage extraction layer captured source-to-target table relationships so the migration output remained auditable and operationally usable.
How the workflow runs
Ingest legacy Hive assets
Load the existing Hive SQL, metadata, and dependency context into the Java 21 conversion engine.
Convert with Azure AI Foundry
Use the Java engine to orchestrate AI-assisted modernization from Hive logic into Iceberg-compatible Spark SQL patterns.
Extract lineage and output
Generate migration-ready SQL and automated table lineage so the transformation remains traceable and operationally safe.
Architecture overview
Business impact
$2M+ vendor cost savings