How to Migrate a Large-Scale Data Ingestion System Successfully

Introduction

Migrating a large-scale data ingestion system is a complex undertaking that requires meticulous planning, robust verification, and incremental rollouts. At Meta, we recently transitioned our entire data ingestion pipeline—responsible for processing petabytes of social graph data from one of the world's largest MySQL deployments—to a new, more reliable architecture. This guide distills the strategies and solutions we used to ensure a seamless migration, from establishing a clear job lifecycle to full deprecation of the legacy system. Whether you're moving from customer-owned pipelines to a self-managed warehouse service or upgrading an existing system, these steps will help you maintain data integrity and operational stability throughout the process.

What You Need

• Detailed inventory of existing ingestion jobs – including data sources, destinations, landing schedules, and resource usage.
• New architecture design – e.g., a self-managed data warehouse service that can operate efficiently at hyperscale.
• Data comparison tools – capable of running row-count and checksum comparisons across old and new systems.
• Monitoring and alerting – for data quality, landing latency, and resource utilization.
• Rollout and rollback mechanisms – feature flags or config toggles that can switch between systems per job.
• Staging environment – to test migration steps without affecting production.
• Communication plan – for stakeholders and downstream consumers of the data.

Step-by-Step Migration Guide

Step 1: Assess Current System and Define Migration Lifecycle

Begin by auditing your existing data ingestion jobs. Document each job’s purpose, data volume, landing-time requirements, and dependencies. Establish a clear migration lifecycle with defined stages: assessment → parallel run → validation → full cutover → deprecation. Each job must pass specific criteria before moving to the next stage. This framework ensures you can track progress and roll back at any point if issues arise.

Step 2: Set Up Comparative Verification for Data Correctness

Before migrating any job, implement a system that runs the new and old pipelines in parallel for that job. Use automated checks to compare outputs:

• Row count comparison – ensure the new system delivers the same number of rows as the old one.
• Checksum comparison – verify that the actual data content is identical (e.g., using hash-based checksums on selected columns or entire tables).

These comparisons must be performed for every job during the validation phase. Any mismatch should trigger an immediate investigation and prevent promotion to the next step.

Step 3: Monitor Landing Latency and Resource Utilization

In addition to data correctness, ensure that the new system does not introduce landing latency regression. The data from the new pipeline should be available within the same or better time window as the legacy system. Also monitor resource utilization (CPU, memory, I/O) to confirm no hidden inefficiencies. Set up dashboards and alerts for these metrics so you can react quickly if performance degrades.

Step 4: Implement Robust Rollout and Rollback Controls

To migrate thousands of jobs without causing wide-scale outages, use feature flags or configuration toggles that allow you to switch individual jobs between the old and new systems. This enables a gradual rollout—migrating a small set of jobs first, then expanding. Equally important, have a one-click rollback plan: if a job fails validation or causes downstream issues, revert it immediately to the legacy system. Test your rollback procedure during the initial pilot.

Step 5: Migrate Jobs Incrementally in Batches

Group jobs by criticality, data volume, or business impact. Start with low-risk, non-critical jobs to validate your process. For each batch:

1. Enable the new pipeline for the batch in a staging environment.
2. Run the verification suite (data quality, latency, resource use).
3. If all criteria are met, promote the batch to production using the rollout controls.
4. Monitor for at least 24-48 hours before moving to the next batch.

This incremental approach limits blast radius and builds confidence in the new system.

Step 6: Validate End-to-End and Deprecate Legacy System

Once all jobs have been migrated and have run successfully in production for a sufficient period (e.g., one full business cycle), you can begin decommissioning the legacy system. Before removing it entirely, conduct a final end-to-end validation across all downstream consumers to ensure no hidden dependencies. Then, systematically turn off the old pipelines and clean up resources. Document the migration outcome and lessons learned.

Tips for a Successful Migration

• Parallel Run Duration: Run both systems in parallel for at least one full data lifecycle (e.g., one day or one week) to catch intermittent issues.
• Automate Validation: Manually comparing results for thousands of jobs is impractical. Invest in automation for data quality checks and latency comparisons.
• Involve Downstream Teams: Communicate migration timelines to teams that consume the ingested data. Give them early access to test with the new system in a staging environment.
• Plan for Failures: Expect unexpected regressions. Have a clear incident response plan that includes rollback procedures and communication channels.
• Iterate on the Process: As you migrate job batches, refine your verification criteria and rollout strategy based on real-world feedback.

By following these steps and tips, you can migrate a large-scale data ingestion system with confidence, ensuring data integrity, and minimal disruption to downstream operations—just as we did at Meta.