When we reach for it
Batch transformation at genuine scale, and pipelines already built on it. Existing platforms built on it too, where the cost of moving off outweighs the cost of running it well.
Spark development
Distributed processing for data too large for one machine — and only for data too large for one machine.
When we would argue against it
Datasets that fit in memory on a large instance, which is most of them. A single process is faster, cheaper and far easier to debug. We check the actual volume before recommending distributed processing, because the honest number is usually smaller than the one in the brief.
What it looks like in delivery
A serious look at whether the job needs a cluster at all before one is provisioned; the answer is often no. Where a cluster genuinely is warranted, jobs are written to be idempotent and restartable — at this scale a failure halfway through is routine rather than exceptional.
Where this appears on the site
Nothing on this site names it yet
We work in Spark, and no case study or service page currently published on this site prints it in its stack. Rather than describe an engagement you cannot check, this space stays empty until one does. Ask us and we will talk you through it directly.
Working in Spark?
Tell us what it is running, what it costs you today, and what you need it to do next. A senior engineer will tell you what we would keep and what we would change.