How we do performance engineering at Fivetran

Fivetran describes how it substantially improved data-pipeline performance after its existing incremental optimization efforts failed to meet an enterprise target of moving 1 TB of data in two hours. Starting from benchmarks of 26 MB/s for Oracle HVA-to-Snowflake and 48 MB/s for Postgres-to-BigQuery, the company ultimately reached 139 MB/s and 259 MB/s respectively, while increasing core production throughput from 7 MB/s to 70 MB/s. Its approach centered on establishing serialized extract volume as a consistent throughput metric, instrumenting every pipeline phase, creating repeatable benchmarks with controlled environments, building visualization and profiling tools, and documenting both gains and regressions. A cross-functional temporary “Tiger Team” was given protected focus and authority across connector, core, destination, infrastructure, and quality-engineering boundaries, enabling system-wide changes such as parallel processing, multithreaded imports, serialization improvements, and connector rewrites. After meeting its goals through roughly 50 projects, Fivetran disbanded the temporary team and formed a permanent Platform Engineering performance group to maintain benchmarks, prevent regressions, improve efficiency and scalability, and support customer-specific performance needs.

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