1,600 Undocumented Tables Cataloged in 3 Days for BigQuery Migration
Post-acquisition metadata discovery accelerated from months to days through AI-powered cataloging, domain analysis, and KPI mapping across 8 undocumented MySQL databases.
Key Metrics
3 Days - Discovery to Delivery
1,600+ - Tables Cataloged
24,000+ - Columns Enriched
50+ - KPIs Identified
THE CHALLENGE
An Australian retailer acquired a regional competitor and needed to consolidate 8 MySQL databases into their Google BigQuery analytics platform. The acquired company had virtually zero documentation: no data dictionaries, no schema descriptions, no business context, and no understanding of how data related across 1,600+ tables and 24,000+ columns. The data engineering team could not design the BigQuery target architecture, plan the migration, or establish data governance without first understanding what existed. The original team members were no longer available.
PAIN POINTS
✖ Zero documentation across 8 MySQL databases with no data dictionaries or schema descriptions
✖ Acquired company’s engineers no longer available for knowledge transfer
✖ No understanding of data domains, relationships, or business context across 1,600+ tables
✖ BigQuery target architecture design blocked without a clear source data inventory
✖ Manual cataloging estimated at 8–12 weeks, delaying the entire integration timeline
THE SOLUTION
3X Data Engineering’s Metadata Intelligence Engine connected directly to all 8 MySQL databases with read-only access, extracted complete schema metadata and data samples, and used AI-powered semantic analysis to generate business context, domain classifications, and relationship maps automatically. Within 3 days, it delivered a comprehensive metadata canvas with object-level documentation, domain analysis, and 50+ KPI recommendations: enabling BigQuery architecture design to begin immediately.
SOLUTION HIGHLIGHTS
✓ Automated discovery across all 8 MySQL databases with full schema extraction, data types, constraints, and sample values
✓ AI-powered semantic inference generating table purposes, column definitions, and business context from naming patterns and data analysis
✓ Intelligent domain classification organizing tables and columns into business domains using graph-based reasoning
✓ Cross-database relationship mapping identifying connections and dependencies across the acquired estate
✓ KPI and analytics identification surfacing 50+ potential KPIs and analytical use cases from the source data structure
✓ Metadata canvas delivered in multiple formats (Excel, JSON, SQL, HTML) for immediate use by data engineering and analytics teams
SAMPLE OUTPUT: AI-ENRICHED METADATA CATALOG
RESULTS
| Traditional Approach | With 3X Data Engineering | |
|---|---|---|
| Metadata Discovery | 8–12 weeks | 3 days |
| Team Required | BAs + data engineers | Lean expert team |
| Domain Analysis | Weeks of SME interviews | Automated in hours |
| KPI Identification | Separate engagement | 50+ KPIs mapped |
| Documentation | Manual spreadsheets | Multi-format export |
| SME Dependency | Critical blocker | Zero dependency |
DELIVERY TIMELINE
DAY 1: Connect & Extract: Read-only access to all 8 databases, automated schema extraction and data sampling
DAY 2: Analyze & Enrich: Semantic inference across 1,600+ tables, domain classification, relationship mapping
DAY 3: Deliver & Extend: Metadata canvas delivered, additional KPI analysis completed within 2 hours of request
ACCELERATORS USED
3X Metadata Intelligence: Automated metadata discovery and enrichment
3X Reverse Engineer: Cross-database dependency mapping