ClickHouse Analytics Foundation.
Schema, ingestion, real-time rollups, and a production analytics system on ClickHouse, engineered for your query and write patterns.
What it is.
A full-lifecycle engagement: Quest1 designs and builds a real-time analytics system on ClickHouse, end to end, shaped to your query and write patterns rather than a generic deployment.
ClickHouse is a column-oriented OLAP database built for real-time analytics: it returns analytical results in under a second on tables with billions to trillions of rows. The MergeTree engine writes sorted, immutable parts on insert and merges them in the background, so heavy writes never block reads. A sparse primary index stays in memory and prunes most of the dataset before a single column is read. Incremental materialized views move aggregation from query time to insert time, which keeps dashboard queries in the millisecond range as data grows. These mechanisms only pay off when the schema, ingestion, and rollup model are designed deliberately around your data — that design is the engagement, and agentic tooling under engineer direction does the implementation against it.
We implement the medallion architecture ClickHouse recommends: raw landing tables, cleaned and conformed intermediate tables, and aggregated serving tables, connected by an incremental materialized-view chain into specialized MergeTree engines. That structure is what makes the system both auditable back to source and fast at the serving layer.
How we engage.
Four phases. Each phase ends when its decisions are made and proven.
Understand the query surface, ingestion sources, and latency targets. Design the medallion data model and the MergeTree schema: ORDER BY by query filters and ascending cardinality, partitioning aligned to retention, data skipping indexes for high-cardinality predicates. The phase resolves when the data model is decided and the trade-offs are named.
Table and engine implementation across the medallion layers (raw, cleaned, aggregated), the ingestion pipeline (ClickPipes for Cloud; Kafka engine to materialized view to MergeTree for OSS; async inserts where write frequency demands it), and the real-time rollup layer using incremental and refreshable materialized views into specialized MergeTree targets. Implementation is done by agentic tooling under engineer direction, in tight review loops so the consequential calls are checked frequently against real data. The phase resolves when the system does what the design said it must.
Integration with your application and dashboards, query optimization against the sparse primary index and skipping indexes, and load validation against realistic ingestion and concurrency. The phase resolves when the system meets the latency and freshness targets under realistic load.
Production cutover, replication and scaling configured, monitoring and alerting to your operational baseline, runbook delivery, and working sessions so your team owns the system.
What you get.
Who this is for.
Teams building a real-time analytics system (product analytics, observability, operational dashboards, fraud or anomaly detection) who want it engineered to their query and write patterns, not assembled from defaults. Teams who've committed to ClickHouse and need the first production system built right, with a data model and rollup layer that hold up as data and concurrency grow. Teams with high-volume ingestion (streaming, CDC, high-frequency writes) who need the pipeline designed deliberately rather than discovered in production. Teams that need the system in production on a real timeline, not an open-ended platform project.
Why Quest1 on ClickHouse.
A real-time analytics system fails or holds up on a handful of decisions made early: the sort order, where aggregation happens, how ingestion batches, what the rollup model assumes. Quest1 engineers these deliberately: ORDER BY, partitioning, and the rollup model are derived from your actual query surface and write profile, and the materialized-view rollup model is designed as a first-class deliverable, not a follow-on. As one of the earliest in the ClickHouse ecosystem, we co-deliver with the ClickHouse engineering team and our engineers are trained from first principles, so the decisions that do not appear in documentation still get made correctly.
Building real-time analytics on ClickHouse?
Tell us about your query patterns, your ingestion sources, and your latency targets. We'll come back with a build plan.