The question worth answering first
Volume alone rarely justifies a distributed platform. A single Postgres instance handles hundreds of millions of rows and tens of gigabytes without complaint, and modern warehouses stretch much further than teams expect. The honest threshold is not a row count — it is when one machine can no longer finish the work inside the window you have.
What usually hurts instead is shape: the same customer recorded three different ways, timestamps in four time zones, a nightly export that silently truncates. Those are integration and modeling problems, and a bigger cluster makes them more expensive rather than solved.
Where we are strongest: moving data between systems that disagree
On EZRater we integrated more than forty insurance carriers whose systems ranged from modern REST APIs to legacy SOAP services to flat-file uploads, each with its own dialect of the ACORD XML standard. One submission went out, comparable quotes came back, and a per-carrier mapping profile absorbed the differences so new carriers could be added without touching core code.
That is the shape of most real data work: not scale, but reconciliation. The same pattern runs through the multi-branch gym platform we built, where membership, door hardware, kiosk payments and retail all had to agree on who a member was.
What we build when the data genuinely is large
Ingestion that survives a bad day: retries, dead-letter handling, and a record of what arrived and what did not. Transformation that is version-controlled and testable rather than a notebook someone runs by hand. Storage modeled for the questions you actually ask, with history kept where it matters.
Then the unglamorous half — monitoring, cost visibility, access control, and documentation someone can read in a year. A pipeline nobody trusts is worse than no pipeline, because people quietly go back to the spreadsheet.
An honest note on our track record
We build data engineering inside products: pipelines, integrations, warehouses and the reporting on top. We have not run petabyte-scale Hadoop or Spark estates, and we would rather say so than imply otherwise.
If your problem genuinely sits at that scale, we will tell you, and bring in specialist help for the platform layer rather than learning it at your expense. In our experience that conversation is rare, and the project underneath is usually a data integration problem wearing a bigger name.
How an engagement starts
A short assessment: what questions the business cannot answer today, which systems hold the pieces, how much data there really is, and how fresh it has to be. We measure rather than assume.
The output is a written recommendation with options, costs and risks — including the option where the answer is a better schema and two scheduled jobs rather than a platform.
