Tackling Our Data Migration Challenge

We approached the migration as an engineering program, not just a database swap. Key goals: zero downtime, no data loss, minimal disruption to our services.

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Our Three-Phase Playbook

1. Dual Writes
   We built our services to write simultaneously to Cassandra and AlloyDB, ensuring AlloyDB stayed in sync from day one.
   
   We deliberately held off on re-modeling data mid-migration. That choice kept our parity checks simple and let us move quickly. Optimization and normalization can come later - first we wanted a clean cutover.
   
   
2. Data Deprecation
   Instead of shoveling everything over, we scoped carefully - focusing only on active datasets. This not only reduced effort, sped up validation, and cut risk, but also aligned with our broader goal of reducing storage by ensuring only live data is maintained going forward.

3. Feature-Flagged Reads
   Reads were switched to AlloyDB behind a feature flag. This lets us roll forward - or roll back - instantly, with no impact to customers.
   
   

Cleanup: Once we had verified stability, we removed the flags and decommissioned Cassandra entirely.

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Data Validation at Scale

To make sure every one of those 2 billion rows landed correctly, we built two layers of checks:

• API Request Replays - replaying logged API calls against both systems and comparing responses.
  
  
• Hash-Based Comparisons - snapshotting tables and verifying record integrity at scale.
  
  

These safeguards let us confirm accuracy with speed and confidence.

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What We Learned

Still, the migration surfaced an important reality of operating at scale: Relational and NoSQL databases behave differently. Cassandra’s eventually consistent model favors availability and hides replication lag behind tunable consistency, while AlloyDB enforces strict transactional guarantees on the primary - making replication lag more visible when reading from read replicas under high load.

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Our monitoring flagged the issue immediately when missing fields appeared in records read right after an update. To fix this, we routed critical reads to the primary, validated the solution, and rolled forward without customer impact. Feature-flagged reads were critical - they let us contain risk, recover quickly, and minimize production code changes.

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Life at Crux After the Move

The payoff is immediate and ongoing:

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• Cleaner Development - relational integrity means less defensive coding.
  
  
• Sharper Observability - AlloyDB’s tooling gives us better visibility into performance.
  
  
• Unified Data Access - one datastore now powers all of our operational metadata.
  
  

The Outcome

• Migrated 2 billion rows with zero downtime.
  
  
• Maintained 100% data accuracy, zero loss.
  
  
• Consolidated to one operational datastore, cutting cost and complexity.
  
  
• Strengthened overall data quality with relational constraints.
  
  

Most importantly, we proved that Crux can execute large-scale migrations without disruption - and deliver the same resilience and precision to our customers’ most critical data pipelines.

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Contact us 👉 here or schedule a demo directly to discover how Crux can help you accelerate your external data and onboarding operations.

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