Big Data Consulting
Architect and operate high-volume, streaming data platforms.
How we approach big data consulting
When volume, velocity, or variety outgrows a traditional database, the architecture has to change, not just the hardware. We design and operate high-throughput, streaming data platforms that stay reliable and queryable as they scale.
We're pragmatic about tooling: the right pipeline, store, and processing engine for your workload and team, with cost and operability weighed alongside raw throughput.
In every engagement
Scope flexes to the problem, but these are the things you can count on us bringing.
- Streaming and batch pipeline architecture
- Data lake and warehouse design
- Throughput and reliability engineering
- Cost and operability optimization
Questions buyers ask about big data consulting
How do we know when we've actually outgrown a traditional database?
When query latency climbs despite indexing, when ingestion volumes force tradeoffs on query performance, or when real-time requirements can't be met by a batch model. Those are the signals. Before redesigning the architecture, we look at whether the database is actually the bottleneck or whether the problem is schema design, missing indexes, or an application layer that's doing work the database should be doing.
Do you recommend a specific stack, or does it depend?
It depends, and anyone who leads with a specific stack before understanding your workload is selling you tools, not solving your problem. The right pipeline, store, and processing engine varies by throughput, latency requirements, team expertise, and how the data gets queried. We weigh cost and operability alongside raw performance because a system the team can't operate isn't a solution.
What does high-throughput mean without made-up benchmark numbers?
It means the platform can ingest and process your actual data volume reliably, at the speed your use case requires, without falling over under load. We don't quote benchmark numbers because they rarely describe your specific workload. We design and test for your data shapes, not a vendor's reference architecture.
How do you keep a big data platform from becoming expensive to operate?
Operability is a design constraint from the start. That means the platform is observable, the failure modes are understood, and the team can diagnose and fix problems without the original architect in the room. Cost comes from over-provisioning and idle infrastructure; we design for the actual load profile and put guardrails on runaway spend.
Industries we know well
The same service, sharpened by the regulations and realities of your sector.
See it on your own data.
Book a 30-minute discovery call and we'll walk through your use case.