Scaling Data Ingestion: How Meta Migrated to a Robust Architecture

Introduction

Meta's social graph, one of the largest MySQL deployments globally, relies on a sophisticated data ingestion system. This system incrementally scrapes petabytes of social graph data daily into the data warehouse, powering analytics, reporting, and downstream products—from day-to-day decisions to machine learning model training. Recently, Meta overhauled this architecture to improve efficiency and reliability at hyperscale, moving from customer-owned pipelines to a simpler, self-managed data warehouse service. The migration was completed with 100% workload transfer and full deprecation of the legacy system. Here, we share the strategies and solutions that made this large-scale migration successful.

The Migration Challenge

As operations grew, the legacy data ingestion system struggled with stricter data landing time requirements, causing instability. The team needed to migrate to a new system while ensuring each job transitioned seamlessly. The challenge involved not only migrating thousands of jobs but also implementing robust rollout and rollback controls to handle issues.

Ensuring a Seamless Transition

To guarantee a smooth migration, the team established a clear migration lifecycle that ensured data integrity and operational reliability at every step. They built strong control mechanisms for rollout and rollback to mitigate risks.

The Migration Lifecycle

The first priority was defining a lifecycle that validated each job before progression. Every job had to meet strict success criteria before moving to the next stage. The key verification steps included:

• No data quality issues: The new system had to produce identical data as the old system. This was verified by comparing row counts and checksums of the output, ensuring complete consistency.
• No landing latency regression: The new system had to deliver data with improved or at least equal latency compared to the legacy system.
• No resource utilization regression: The new architecture had to maintain or reduce resource consumption, preventing overloading of infrastructure.

Each job underwent these checks before being allowed to proceed to the next phase of the lifecycle. This systematic approach minimized risks and enabled the team to handle issues early.

Rollout and Rollback Controls

Throughout the migration, the team maintained the ability to quickly roll back any job if a regression was detected. This safety net was crucial for maintaining system stability while gradually transitioning thousands of pipelines.

Conclusion

Meta's migration to a self-managed data warehouse service demonstrates how careful planning, rigorous verification, and robust controls can enable a seamless transition at hyperscale. The new architecture has significantly enhanced efficiency and reliability, powering Meta's data-driven operations. For engineering teams facing similar challenges, the key takeaways are clear: establish a thorough migration lifecycle, prioritize data integrity, and invest in rollback capabilities.