G.2 · Enterprise Platform & Data Engineering
Data Platforms and Streaming Analytics
Lakehouse and stream processing with sub-second freshness
What we build
Data platforms built for freshness and lineage, not volume alone. Includes lakehouse storage on Iceberg or Delta, change-data-capture ingestion from operational systems, streaming transformation in Flink or Spark, columnar and time-series serving layers, metric and semantic definitions held in one place, column-level lineage end to end, and contract tests that fail a pipeline before a dashboard reports a wrong number.
Capabilities
- Lakehouse storage on Iceberg or Delta, with schema evolution that does not break readers
- Change-data-capture ingestion from operational systems without dual writes
- Streaming transformation in Flink or Spark with exactly-once sinks
- One place for metric and semantic definitions, consumed by every downstream tool
- Column-level lineage and contract tests that fail a pipeline before a dashboard lies
Related services
How it connects
Where it sits in the stack.
This system, and the two it hands off to. None of them can be optimized alone.
Data Platforms & Streaming
Data platforms built for freshness and lineage, not volume alone.
Distributed Backend Systems
Transactional cores designed for correctness under concurrency.
Enterprise Platforms · see serviceReliability Engineering
The measurement and failure discipline that keeps a platform inside its availability target.
Enterprise Platforms · see serviceBring us the whole stack.
Tell us where latency is costing you, from the die to the data center to the control room. An architect replies with a first read of the problem, not a sales deck.