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Analytical backends for large datasets.

When analytics slow down with growth, the first question is the workload. We select the query engine with benchmarks on your data, then design the backend around it.

For teams whose analytics no longer keep up.

Request a review Read: choosing a query engine

Signs the backend, not the query, is the problem.

Queries that were instant now take seconds, and it keeps getting worse as data grows.The aggregates that matter most, wide scans over large tables, are the slowest.Analytical and transactional load are fighting over the same database.You are weighing ClickHouse, DuckDB, Postgres, and DataFusion, and the choice is expensive to reverse.

How we approach it.

The choice of a storage and query system is rarely neutral. We make it deliberately, and build the architecture around it.

01

Understand the workload

Point lookups, wide scans, or high-QPS aggregates. The access pattern, not the trend, decides the engine.

02

Select with evidence

Options benchmarked against your real data, not blog posts, with a recommendation you can defend.

03

Design the backend

Columnar storage and query engines tuned for interactive speed at volume, with clear boundaries.

04

Prove it in production

Benchmarks, observability, and a design your team can run and evolve without us.

StackClickHouse/DataFusion/Ballista/Apache Arrow/Parquet/SQL

What you leave with.

An engine choice you can defend
Backed by benchmarks on your data, with the trade-offs written down, not asserted.
Decision records that outlast us
The reasoning behind the design, captured so your team can defend it in six months.
Performance that holds as data grows
A backend designed for predictable, interactive speed at volume, not just today's row count.

Facing an engine decision?

We run engine-selection reviews and build the backend around the result. See how we work in consulting and delivery.

Request an architecture review