Client Details
A global automobile manufacturing leader, operating across multiple continents with a diversified product line ranging from passenger vehicles to electric and autonomous models. The organization relies on real-time data from IoT-enabled factories, connected vehicles, and supply chain ecosystems to drive innovation, ensure quality, and meet regulatory standards.
Challenge
The company’s legacy Hive Metastore architecture had become a bottleneck due to its decentralized nature, maintained separately by regional engineering and operations teams. This fragmentation led to metadata inconsistencies, schema drift, and data silos, severely limiting the enterprise’s ability to apply governance and data lineage controls at scale.
The increasing adoption of machine learning (ML) and digital twin technologies across R&D and manufacturing lines required tighter integration of data assets with advanced analytics platforms. However, managing access control, ensuring schema consistency, and enabling lineage tracking across multiple domains had become unmanageable with the legacy stack.
Solution
A comprehensive modernization initiative was launched to migrate 194+ schema-intensive tables from Hive Metastore to Unity Catalog using Azure Databricks. The migration strategy integrated complex data engineering workflows involving:
- Automated schema introspection and translation using Apache Atlas APIs and Delta Lake transaction logs to maintain metadata integrity.
- PySpark-based transformation pipelines, augmented with Databricks REST APIs, to ensure seamless ingestion into Unity Catalog with versioned lineage tracking.
- Delta Live Tables (DLT) for building declarative ETL pipelines, enabling continuous data quality enforcement and monitoring during and after migration.
- Custom Apache Airflow DAGs orchestrated within Azure Data Factory (ADF) for dependency-aware, cross-environment metadata sync jobs between legacy Hive and Unity Catalog.
- Unity Catalog audit logging integrated with Azure Purview and Microsoft Defender for Cloud to extend governance, security monitoring, and regulatory compliance across the data estate.
- Utilization of dbt (Data Build Tool) to refactor and modularize data models, allowing better abstraction and reuse of data transformation logic across business domains.
In addition, the solution adopted role- and attribute-based access control (RBAC + ABAC) models within Unity Catalog to enforce granular access policies based on user roles, regions, and project affiliations significantly improving data security posture.
The Impact
✅ Enterprise-Grade Governance: Achieved centralized metadata control with complete visibility across manufacturing, R&D, and supply chain analytics.
✅ Data Mesh Enablement: Enabled domain-oriented data ownership and discoverability, aligned with a federated data governance model.
✅ Operational Streamlining: Decommissioned fragmented Hive deployments and significantly reduced effort required for metadata reconciliation and troubleshooting.
✅ Advanced Analytics Readiness: Positioned the enterprise for scalable AI/ML development by aligning data governance with MLOps and ModelOps standards.
