Meta Completes Massive Data Ingestion Overhaul, Migrates Petabytes of Social Graph to New System

Breaking News: Meta’s Data Ingestion System Fully Migrated

Meta has successfully migrated all its data ingestion workloads from a legacy pipeline system to a new, self-managed data warehouse architecture, the company announced today. The move, which involved scraping and processing petabytes of social graph data daily, is now complete with the legacy system fully deprecated.

“This migration was critical to maintaining reliability at our scale,” said a Meta senior infrastructure engineer. “We needed a system that could handle the strict data landing time requirements without instability.”

The new architecture replaces customer-owned pipelines with a simpler, centralized service that operates efficiently even at hyperscale. The transition was achieved without any data quality issues or latency regressions.

Background: Why Meta Revamped Its Data Ingestion System

Meta’s social graph relies on one of the world’s largest MySQL deployments. Every day, the data ingestion system incrementally scrapes several petabytes of data from MySQL into the data warehouse for analytics, reporting, and machine learning.

The legacy system, based on customer-owned pipelines, functioned well at small scale but became unstable as operations grew. “We observed increasing instability under strict data landing time requirements,” the engineer explained. “A fundamental architectural change was necessary.”

What This Means: A More Reliable Foundation for Meta’s Products

With the new system, Meta can now deliver up-to-date snapshots of the social graph more reliably. This powers everything from day-to-day decision-making to ML model training and product development across the company.

The migration also sets a precedent for future large-scale system changes at Meta. “The solutions and strategies we developed—tracking job lifecycles, rigorous verification, and robust rollback controls—will be reused for other migrations,” the engineer noted.

The Migration Challenge: Ensuring Seamless Transition

Migrating thousands of jobs required careful planning. Each job had to be verified for correctness and meet defined success criteria before progressing through the migration lifecycle.

Key verification steps included: no data quality issues (confirmed by comparing row count and checksum between old and new systems); no landing latency regression (new system had to match or improve performance); and no resource utilization regression.

“We put robust rollout and rollback controls in place to handle any issues that might arise,” the engineer added. The result: 100% workload transition with zero data loss or downtime.

For more details on the technical strategies, see the background section or what this means for users.