ClickHouse engineering

ClickHouse architecture, migration, and performance engineering.

We help teams decide when ClickHouse fits, design the architecture around the workload, and make production systems fast, reliable, and operable.

When to call us.

Scans and aggregates are slowing down, or analytical load is interfering with transactional work.

The choice is expensive to reverse and must be validated on your data before committing to the move.

Schemas, ingestion, memory, concurrency, or queries prevent the system from reaching its targets.

Observability, backups, failure modes, and cost behavior need to become explicit.

From the initial decision to a system your team can operate.

Topology, ClickHouse Cloud or self-managed, replication, sharding, scaling, and workload isolation.

Schemas, partitioning, ordering keys, projections, materialized views, and read/write trade-offs.

Batch, streaming, CDC, throughput, backpressure, deduplication, and data-quality controls.

Query profiles, memory, concurrency, p95/p99, and reproducible benchmarks on representative data.

An incremental path from PostgreSQL, a warehouse, or another analytical backend, with validation and rollback.

Observability, backups, failure modes, runbooks, and compute/storage trade-offs.

Every recommendation must be explainable, measurable, and transferable to your team.

Critical queries, volumes, concurrency, growth, and operating requirements.

A reproducible protocol and baseline for the paths that actually matter.

Documented architecture and trade-offs, including when another solution is a better fit.

Implementation, tests, observability, runbooks, and knowledge transfer.

A defensible architecture

A reproducible protocol and measurements

A migration or optimization plan

Code, tests, and runbooks when we implement

Share the critical queries, volumes, p95/p99 targets, and where the system stops keeping up.