Everything you can attach
Logs and exports up to your deployment limit, optional code files, or paste long SQL / PySpark / Scala directly. The more you share (logs + code), the sharper the recommendations.
Built for production Spark teams
Send us what you already have: Spark / Hadoop event logs (plain text or .gz), Spark UI exports, driver or executor log snippets, plus your job context as files or paste —
PySpark scripts and notebooks, Spark SQL, Scala (and raw .txt if needed). Mix a log upload with pasted code so the brief covers both the run and the intent.
You get a prioritized tuning playbook: partitions, shuffle, skew, memory, broadcast joins, AQE, and stage bottlenecks — written so your team can ship changes the same day.
Sign in with Google to open the analyzer and upload your artifacts.
*Reported by customers and internal before/after benchmarks on comparable workloads. Your results depend on cluster shape, data skew, and how fully you apply the playbook.
Trusted in production. Data platform teams at Fortune 500 banks, insurers, retail media, and telecom groups — plus late-stage unicorns in fintech and ad tech — use this loop to keep petabyte-scale Spark fast without hiring a dedicated performance SWAT team.
Logs and exports up to your deployment limit, optional code files, or paste long SQL / PySpark / Scala directly. The more you share (logs + code), the sharper the recommendations.
Targets real money and SLA time: less shuffle spill, better partition counts, broadcast vs shuffle joins, executor memory, and stage skew — not generic “best practices” bullet lists.
Google OAuth for access control; recommendation history when enabled on your instance. Large orgs plug the same flow into existing identity and compliance reviews.
Sign in, upload your log and code, and get a ranked optimization brief in minutes.
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