ClickHouse Analytics Engine — Real-Time OLAP at Scale

I build fast analytical databases on ClickHouse that answer questions over billions of rows in well under a second. Every project starts with the data model: I design the schema, choose sort keys and partitioning, and pick the right engine — MergeTree, ReplacingMergeTree or AggregatingMergeTree — so your common queries scan as little data as possible.

Then I set up ingestion that keeps pace with your event stream, from a message queue, object storage or a database change feed. Batching, deduplication and back-pressure keep inserts cheap and lossless during spikes, materialized views pre-aggregate on write, and tuned TTLs keep hot and cold data where they cost the least.

Finally I wire everything to the dashboards and APIs your team already uses, validate the numbers against your source of truth, and load-test with realistic concurrency. You get documented schemas, reproducible migrations and query patterns your engineers can extend without me.

What you get:
— Schema, partitioning and sort-key design matched to your queries
— Streaming or batch ingestion with deduplication and monitoring
— Materialized views and TTLs for sub-second rollups