Big Data Engineering Services — Trillions of Rows, Millisecond Queries

DiveScale has operated data systems at scales that expose every assumption: trillions of rows across sharded databases where a wrong partition strategy collapses query performance, ETL pipelines processing billions of events daily, and warehouses where a poorly tuned materialized view becomes a $40k/month bill. We have solved those problems in production — not in benchmarks.

Our sharding experience goes beyond the standard advice. We design custom sharding keys around real query patterns, build cross-shard aggregation layers that keep application code clean, and instrument shard-level performance so hot spots are visible before they become incidents. Queries that touch trillions of rows come back in milliseconds when the data model and infrastructure are aligned.

ETL is where most data projects fail silently. We build pipelines with lineage from day one — every transformation tracked, every source version recorded, every anomaly surfaced before it reaches the dashboard. A pipeline that runs daily is not production until it handles late data, schema drift, and upstream failures without manual intervention.

We design around the workload — batch warehouse for stable reporting, lakehouse (Iceberg, Delta, or Hudi) when you need open formats and engine choice, streaming when freshness genuinely matters. We do not chase the latest shiny architecture; we match the shape to the problem and cost-manage it from the start.

And we treat data as a product: contracts, ownership, freshness SLAs, lineage from source to dashboard. Without those, every new question costs an analyst a day of detective work.