At a certain scale the architecture has to change rather than the hardware. We design for the volume and latency you genuinely have — distributed processing where it is warranted, storage formats and partitioning that keep scans affordable, and a cost model that does not surprise you at month end.
Data & Analytics
Big Data
Processing at a volume where the usual approaches stop working.
Overview
What the engagement covers
- Distributed processing with Spark or its equivalent, sized to the job
- Storage formats and partitioning chosen against real query patterns
- Streaming where latency requires it, batch where it does not
- Cost controls on compute that can otherwise scale without limit
- Job tuning against measurement rather than against folklore
What you leave with
- Processing that completes inside its window at full volume
- Cost per job that is known and controlled
- Headroom for the volume you expect next year
Data & Analytics
The rest of this practice
Big Data, scoped to your estate
Tell us where you are and what it has to be worth. We will come back with a scope, a sequence and a number.