ClickHouse for real-time analytics
ClickHouse is an open-source, column-oriented SQL database built to analyze large volumes of continuously arriving data with low latency. Its speed, efficient storage, and high query concurrency make it particularly well suited to event streams, logs, sensor data, observability, and customer-facing analytics. We use it when traditional databases or cloud warehouses become too slow or expensive for the workload.
What it is good at
High-volume event, log, and telemetry analytics
Continuous ingestion and near-real-time analytical workloads
Pre-aggregation and materialized views
Efficient storage and data retention
Flexible cloud and self-managed deployment
Customer-facing analytics with high concurrency
Observability and AI-agent telemetry
What we deliver with ClickHouse
Real-time analytics on event streams
We build analytical workloads on data that keeps arriving: application events, transactions, logs, and message queues. We define the ingestion path, the freshness target the business needs, and what happens to late, duplicated, and out-of-order events.
Schema, sorting keys, and partitioning
Query performance in ClickHouse depends heavily on table design, not just the size of the cluster. We design the schema, sorting keys, partitioning, and indexes around the queries the workload has to serve, and we document the access patterns they assume.
High-volume ingestion
We design ingestion for the volume and arrival pattern of the source, using the appropriate table engines, batching, and buffering. We define how the pipeline behaves under backpressure, retries, and replay, so a source outage does not turn into a data-quality incident.
Customer-facing analytics
We build analytics that sit inside a product and are queried by customers, not by an internal reporting team. That makes concurrency, per-tenant isolation, response-time targets, and access control engineering requirements rather than reporting preferences.
Cost and latency engineering
ClickHouse is fast when table design fits the query pattern. It is not fast by default, and it is not cheap by default. We use pre-aggregation, materialized views, TTL rules, tiered storage, and retention policy to hold latency where the business needs it and to stop paying to keep data nobody queries.
Migration with reconciliation
We move analytical workloads to ClickHouse where the latency or cost case is clear. We validate the migration against critical queries, historical periods, data volumes, and agreed reconciliation criteria before cutover.
Deployment, security, and operations
We compare managed and self-managed deployment against security, cost, control, and your team’s operational capacity, then configure the cluster, users, roles, and network access accordingly. We deliver monitoring, upgrade and backup verification procedures, runbooks, and the hands-on enablement a team needs to own the platform.
Cadvel is a ClickHouse Ignite Partner
Partnership status
