AI Metadata Discovery: 1,600 MySQL Tables Cataloged in 3 Days for a Post-Acquisition BigQuery Migration
An Australian retailer absorbing an acquired regional competitor needed to consolidate 8 undocumented MySQL databases into Google BigQuery. The 1,600 tables and 24,000 plus columns had no documentation, no business context, and the SMEs had left the company. AI-powered metadata intelligence produced a complete catalog and domain classification in 3 business days of accelerator runtime, unblocking the BigQuery migration program.
Quick facts
| Industry | Retail |
|---|---|
| Engagement type | Post-acquisition data discovery |
| Source platform | 8 MySQL databases (acquired company) |
| Target platform | Google BigQuery |
| Engagement scope | Automated metadata catalog + domain analysis |
| Timeline | 3 business days runtime, 5 business day total engagement |
| Estate analyzed | 1,600 tables, 24,000+ columns |
| Comparable manual effort | 8 to 12 weeks, 4+ analysts |
| Accelerators used | Metadata Intelligence, Reverse Engineer |
Challenge
The retailer acquired a regional competitor and needed to integrate 8 MySQL databases into its existing Google BigQuery analytics platform. The acquired company left behind no documentation. No data dictionaries. No schema descriptions. No business context. The original engineers and analysts had departed during the acquisition.
The data engineering team faced a hard block. They could not design the BigQuery target architecture, plan the migration, or establish data governance without first understanding what data existed, how it was structured, and what each table represented. The estate spanned 1,600 tables and 24,000 plus columns across 8 databases. Domain ownership was unclear. Relationships between tables had to be inferred.
The traditional path was 8 to 12 weeks of analyst interviews (with SMEs who no longer worked at the company), schema reverse engineering, and manual business logic documentation. The business integration timeline was at risk.
Approach
3XDE deployed the Metadata Intelligence and Reverse Engineer accelerators against the 8 MySQL databases under read-only access.
Five capabilities ran in sequence.
- Rapid automated discovery connecting directly to all 8 MySQL databases and extracting schema metadata, data samples, and relationship patterns
- AI-powered semantic inference analyzing table names, column names, data types, and data patterns to generate business context
- Comprehensive metadata catalog including object-level documentation, constraints, relationships, and sample values
- Intelligent domain classification grouping tables and columns into business domains using graph-based reasoning
- KPI and analytics insights identifying candidate metrics and analytical use cases based on data structure and content
Every output was reviewed by a senior architect before delivery.
Implementation
Day 1
Configured read-only connections to all 8 MySQL databases. Validated access. Defined scope. Initiated automated metadata extraction.
Day 2
Metadata Intelligence accelerator processed 1,600 tables and 24,000 plus columns. Performed semantic inference on table names and column data. Generated business context. Built cross-database relationship maps automatically.
Day 3
Delivered the comprehensive Modernization Canvas with complete data dictionary, domain classifications, lineage maps, and initial findings. Conducted a review session with the client's data team. The team requested additional KPI analysis; the accelerator reprocessed with extended parameters and delivered the additional output within 2 hours.
Days 4 to 5
Final handover. Metadata catalog exports in Excel, JSON, SQL, and HTML formats. Trained the client team to navigate and update the canvas. Enabled self-service metadata queries for ongoing use.
Results
- Complete metadata catalog delivered in 3 business days against the 8 to 12 week manual estimate
- 1,600 tables and 24,000 plus columns documented with object-level granularity
- Data domain analysis and 50 plus candidate KPIs delivered within hours of request
- BigQuery target architecture design unblocked and proceeded on schedule
- Traditional discovery effort of 4+ analysts for 8 to 12 weeks replaced with a fixed-scope 5-day engagement
- Migration planning compressed by approximately 10 weeks
- Permanent metadata asset retained by the team for ongoing data management
- Zero dependency on departed SMEs
What this means for you
This pattern applies to any post-acquisition data integration, undocumented legacy estate, or pre-migration discovery where the SMEs are unavailable. The fixed-scope engagement produces a permanent metadata asset your team continues to use after the migration completes.