The Challenge
Data warehouse consolidation and migration projects are among the most complex undertakings a data team faces. Before a single row of data moves, you need a thorough understanding of what exists, where it lives, and how it’s structured.
Manual discovery creates real problems:
- Large organisations may have dozens of BigQuery projects with hundreds of datasets accumulated over years
- Schema documentation is often outdated, incomplete, or simply doesn’t exist
- Understanding data volumes requires running COUNT queries across every table—an enormously time-consuming task
- Forgotten or orphaned datasets from legacy projects create migration scope ambiguity
- Without a systematic inventory, migration estimates are guesses and risk is unquantified
Teams that skip thorough discovery pay for it later with project overruns, missed dependencies, and broken downstream systems.
The Autohive Solution
Autohive’s Google BigQuery integration automates the discovery and documentation phase of warehouse consolidation projects. By programmatically retrieving metadata across all your projects, you get a complete, accurate inventory in a fraction of the time it would take manually.
Cross-project Discovery
Autohive lists all BigQuery projects you have access to, then iterates through each one to enumerate datasets. What would take days of manual work completes automatically, ensuring nothing is missed.
Comprehensive Dataset Metadata
For each discovered dataset, retrieve detailed metadata including creation time, location, labels, and access controls. Understand how datasets are organised and governed before deciding how to consolidate or migrate them.
Table Schema Extraction
Pull full schema definitions for every table—column names, data types, modes, and descriptions. Automatically document your current warehouse structure as a baseline for migration planning and target schema design.
Data Volume Analysis
Execute automated SQL COUNT queries against each table to understand record volumes. Combine schema and volume data to prioritise migration sequences, estimate transfer costs, and plan cutover windows.
Benefits
- Complete warehouse visibility – Every dataset and table accounted for across all projects
- Accurate migration scoping – Real schema and volume data replaces guesswork in project estimates
- Automated documentation – Discovery output becomes living documentation of your pre-migration state
- Faster project kickoff – Weeks of manual discovery compressed into hours of automated inventory
- Reduced migration risk – Dependencies and orphaned data identified before cutover, not during
How It Works
- Enumerate projects – Autohive lists all BigQuery projects accessible to your service account
- Discover datasets – For each project, the agent lists all datasets and captures their metadata
- Extract table schemas – For each dataset, retrieve all tables along with their full schema definitions (columns, types, modes)
- Measure data volumes – Execute SQL queries against each table to capture record counts and understand data distribution
- Compile the inventory – Aggregate all metadata, schemas, and volume figures into a structured report
- Inform migration planning – Use the inventory to map source-to-target schema transformations, identify data quality issues, and sequence migration waves
Getting Started
- Sign up at app.autohive.com
- Connect the Google BigQuery integration from the Autohive marketplace
- Grant your Autohive service account read access to all relevant BigQuery projects
- Configure the discovery workflow with your project scope
- Run the inventory and receive a complete picture of your warehouse ready for migration planning
