Cataloging 1,600 Undocumented Tables in 3 Days for a Post-Acquisition Retail Integration

Post-acquisition retail integration depends on knowing what the acquired data estate actually contains before target design begins. For an Australian retailer, that visibility was missing. The acquired company ran across eight MySQL databases, the estate was undocumented, and no subject matter experts were available to explain table purpose, structure, or meaning. The target was Google BigQuery, but the integration could not proceed until the team had a documented source foundation. The engagement used Metadata Intelligence and Reverse Engineer accelerators directly against the undocumented sources. Instead of relying on interviews, the work read the systems themselves, extracted metadata, profiled the data, and recovered structure and meaning from the databases as they existed. In 3 business days, the team documented 1,600 tables and more than 24,000 columns, delivered more than 50 candidate KPIs within hours of the request, unblocked the BigQuery target architecture, and retained a permanent metadata asset for ongoing governance.
3 Business Days

Metadata discovery completed

8 MySQL Databases

Source estate

1.6K Tables / 24K+ Columns

Documented

50+ KPIs

Delivered within hours of the request

THE CHALLENGE

An Australian retailer had acquired a competitor and needed to integrate the acquired company's data estate, which ran across eight MySQL databases. The estate was undocumented, and no subject matter experts were available to explain it. The target was Google BigQuery, and the integration could not proceed until the team understood what the acquired estate actually contained.

THE SOLUTION

The engagement applied Metadata Intelligence and Reverse Engineer accelerators directly against the undocumented sources. Because no SMEs were available, discovery could not rely on interviews. It had to read the systems themselves: extracting metadata, profiling the data, and recovering structure and meaning from the databases as they existed.

Cataloging 1,600 undocumented MySQL tables in 3 days for a post-acquisition retail integration to Google BigQuery, showing source estate, metadata intelligence, reverse engineering, and delivered metadata outputs.

WHAT WAS DELIVERED

  • 1,600 tables and more than 24,000 columns documented.
  • More than 50 candidate KPIs delivered within hours of the request.
  • A BigQuery target architecture unblocked so the integration could proceed on schedule.
  • A permanent metadata asset retained for ongoing governance.

WHY IT WORKED

The constraint was time and the absence of tribal knowledge, and automated metadata discovery addressed both. Cataloging 1,600 tables and 24,000 columns by hand would have taken weeks the integration did not have. Reading the estate directly compressed that to three business days and produced a documented foundation the team could design against immediately.

THE LASTING VALUE

Beyond unblocking the immediate integration, the catalog became a durable governance asset. The knowledge that did not exist in any person now existed in a maintained form, which is exactly what an organization needs after an acquisition when the people who understood the acquired systems are not part of the team.

ACCELERATORS USED

Metadata Intelligence: Source-connected metadata extraction, data profiling, and documentation across undocumented MySQL sources.

Reverse Engineer: Structure and meaning recovery from the acquired databases when SMEs were unavailable.

KEY TAKEAWAY

Need to catalog an undocumented source estate before post-acquisition BigQuery integration?

3X Data Engineering read the acquired estate directly across eight MySQL databases, extracted metadata, profiled the data, and recovered structure and meaning without relying on SME interviews.

The result was a documented metadata foundation covering 1,600 tables and 24,000+ columns in 3 business days, with 50+ candidate KPIs delivered, BigQuery target architecture unblocked, and a permanent governance asset retained.

Frequently Asked Questions

Answering common questions about 3X Data Engineering to help you get started on your modernization journey.

3X Data Engineering documented 1,600 tables and more than 24,000 columns across the acquired company's eight MySQL databases.
The acquired estate was undocumented and no subject matter experts were available, so discovery had to read the systems directly instead of relying on interviews.
The engagement used Metadata Intelligence and Reverse Engineer accelerators to extract metadata, profile data, and recover structure and meaning from the undocumented sources.
The documented metadata foundation unblocked BigQuery target architecture planning and created a permanent metadata asset for ongoing governance.

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Catalog Undocumented Source Estates Before BigQuery Migration

Use source-connected metadata discovery to document tables, columns, structure, and candidate KPIs when SMEs and data dictionaries are unavailable.

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