Pipelines that make terabytes usable in real time — without waking anyone up.
Big data pays off when it’s usable. We build pipelines that ingest, clean, and land data reliably — batch when you can, streaming when you must — so analytics and ML have a foundation they can trust.
It’s the right fit for teams outgrowing spreadsheets, engineering platforms that need real-time analytics, or businesses drowning in event data.
Kafka, Kinesis, and Flink where latency matters — batch where it doesn’t.
Every engagement ships against outcomes we agree up front.
Batch or streaming, orchestrated and observable.
Event pipelines with sub-second latency where needed.
Warehouse or lakehouse designed for how you actually query.
Reliable transforms with lineage and tests.
Sized for growth, not surprise bills.
Clean, documented, and modeled for BI.
A real sequence — each step earns the next.
Every source, its shape, and its owner catalogued.
Pipeline, warehouse schema, and SLAs proposed up front.
Pipelines built, tested, and monitored.
Runbooks, alerts, and an owner for the pipeline.
Concrete artifacts you take away from the engagement.
We pick the pattern that fits — not the trendy one.
Tests and lineage built in from day one.
Warehouse spend is a first-class metric.
A senior data engineer on every project.
We mix them — batch is cheaper and simpler; streaming is for real latency needs.
Yes — Snowflake, BigQuery, Redshift, Databricks; or on-prem.
Not really; we design for growth up front.
Access control, PII masking, and lineage — supported and encouraged.
Depends on volume and complexity; we scope with a fixed estimate.
Tell us what you’re building — we’ll reply within one business day.