Legacy Databricks to E2 Platform Migration
DriveWealth, a pioneering FinTech company that provides fractional share investing infrastructure to global partners, was operating on the legacy Databricks platform. This older architecture lacked critical capabilities available on the E2 platform — including AutoML, Feature Store, optimized Databricks SQL, and the full Unity Catalog governance suite. Additionally, the Hive Metastore's decentralized governance model was creating security and data discovery challenges that could not be solved without migration. Vrahad Analytics led a carefully orchestrated dual-track migration: transitioning all jobs to the E2 platform while simultaneously migrating the entire Hive Metastore to Unity Catalog — all without disrupting DriveWealth's mission-critical financial data pipelines that process billions of dollars in transactions.
Client
DriveWealth
Cloud Platform
AWS
Duration
4 Months
The Challenge
DriveWealth's legacy Databricks deployment had served the company well during its growth phase, but was now holding back the data team's ability to leverage modern platform capabilities. For a FinTech handling real financial transactions, any migration carried inherent risk that demanded meticulous planning.
The legacy Databricks platform lacked access to E2-exclusive features like AutoML, Feature Store, and the enhanced Databricks SQL engine, preventing the data science team from building and deploying ML models with modern tooling.
The Hive Metastore's workspace-level governance created data silos — teams in different workspaces couldn't discover or access data from other workspaces without complex workarounds, impeding cross-team analytics.
Job configurations built over years had accumulated technical debt — hardcoded paths, deprecated APIs, and legacy cluster configurations that would need to be updated for E2 compatibility.
Financial data pipelines processing real-money transactions required the highest level of reliability during migration — even brief disruptions could have regulatory and financial consequences for DriveWealth and its partners.
The lack of centralized data lineage and audit trails on the legacy platform made it difficult to comply with financial regulatory requirements (SEC, FINRA) for data provenance and access tracking.
Existing streaming workloads used legacy managed streaming configurations that needed to be redesigned for the E2 platform's different networking and security architecture.
Our Solution
We executed a dual-track migration strategy: simultaneously migrating the Hive Metastore to Unity Catalog for centralized governance and transitioning all jobs to the E2 platform for access to modern capabilities. Every step was designed with financial-grade reliability requirements in mind, ensuring zero impact to production transaction processing.
Performed a comprehensive inventory of all Databricks assets — jobs, notebooks, clusters, libraries, secrets, mount points, and Hive Metastore objects — creating a complete dependency map that guided the migration sequence.
Redesigned job configurations for E2 compatibility, updating deprecated APIs, modernizing cluster specifications, replacing hardcoded mount paths with Unity Catalog references, and adding proper error handling and retry logic.
Built automated migration scripts that handled the bulk transfer of Hive Metastore objects (databases, tables, views) to Unity Catalog, preserving all metadata, permissions, and table properties while adding lineage tracking.
Implemented a shadow-run approach where migrated jobs ran in parallel on both legacy and E2 platforms, with automated output comparison to verify functional equivalence before switching over production traffic.
Migrated streaming workloads to E2's managed streaming infrastructure, redesigning network configurations and security policies to work with the E2 architecture's VPC-level isolation model.
Conducted thorough post-migration testing including financial reconciliation checks, ensuring that all transaction processing pipelines produced identical results on the E2 platform to within zero tolerance.
Implementation Phases
Asset Inventory & Dependency Mapping
3 WeeksCataloged every Databricks asset, mapped inter-job dependencies, identified legacy API usage and deprecated configurations, and produced a prioritized migration plan that minimized risk to financial data pipelines.
E2 Workspace Provisioning & Configuration
2 WeeksProvisioned the E2 workspace with enhanced security configurations (VPC peering, private link, CMK encryption), set up Unity Catalog, configured IAM roles, and established the networking layer for streaming workloads.
Hive Metastore to Unity Catalog Migration
4 WeeksMigrated all Hive Metastore databases, tables, and views to Unity Catalog using automated scripts with validation at each step. Implemented fine-grained access controls and verified data integrity for every migrated object.
Job Migration & Redesign
5 WeeksUpdated and migrated all jobs to E2, redesigning configurations for the new platform. Ran shadow executions in parallel to verify functional equivalence. Batch jobs, streaming jobs, and ML pipelines each had their own migration track.
Validation, Cutover & Decommission
3 WeeksPerformed comprehensive financial reconciliation, executed phased cutover from legacy to E2, verified all regulatory compliance requirements, and decommissioned the legacy platform with full audit trail documentation.
Technologies Used
Key Results
Measurable outcomes and business impact delivered through this engagement.
Completed full platform migration from legacy Databricks to E2 with zero impact to production financial data pipelines, verified through comprehensive reconciliation checks with zero tolerance for discrepancies.
Migrated the entire Hive Metastore to Unity Catalog, establishing centralized governance with fine-grained access controls, complete data lineage, and audit trails that meet SEC and FINRA regulatory requirements.
Unlocked advanced capabilities including AutoML and Feature Store, enabling DriveWealth's data science team to build, train, and deploy ML models 3x faster using integrated platform tools rather than external workarounds.
Improved cost efficiency through better resource allocation on E2, with optimized compute configurations and auto-scaling policies that reduced average cluster costs by 25% while improving job performance.
Enhanced performance for BI, SQL, and streaming workloads with E2's optimized Databricks SQL engine delivering 2-4x faster query execution for analytical dashboards used by the business team.
Eliminated cross-workspace data silos, enabling seamless data discovery and sharing across teams through Unity Catalog's centralized data catalog with search, tagging, and documentation capabilities.
Before vs. After Comparison
Migration Completeness
Before
Legacy platform
After
100% on E2
Full migration
Governance Model
Before
Hive (workspace-level)
After
Unity Catalog (org-level)
Centralized
ML Model Development
Before
External tools
After
Native AutoML
3x faster
SQL Query Performance
Before
Baseline
After
2-4x faster
Optimized SQL engine
Cluster Costs
Before
Baseline
After
25% lower
Better allocation
Data Discoverability
Before
Workspace-siloed
After
Org-wide catalog
Cross-team access
Migration Scale — Assets Migrated to E2
Workload Type Distribution
Platform Capability — Legacy vs E2
Cumulative Assets Migrated to E2
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