Building a Unified Data Platform with ClickHouse

We've built a unified data platform with ClickHouse that merges operational and analytical needs. Data powers every software system, but traditional systems often split into separate analytical and operational systems. We thought hard about this as we started Synq, and we power our entire user-facing application, internal analytics, data reliability monitoring alerts, in-application analytics, and machine learning models from a single platform - ClickHouse. To find the right platform, we set ambitious performance goals for backfilling data, created flexible internal transformations, and built a single platform that could store raw log data and act as a serving layer for most data use cases needed by our applications and APIs. We chose ClickHouse due to its elastic ingest performance, ability to create query-specific data models with Materialised Views, and stable infrastructure. Our ingestion microservice uses the Async API to avoid buffering in application code, and we've settled on the final approach after consultation with the ClickHouse team. With reliable and scalable ingestion in place, we can leverage Materialised Views to optimize performance. We've created specialized tables that transform our raw logs data to a format optimized for our queries, supported by adequate partitioning and ordering keys. Data between raw logs and specialized tables are kept in sync in real-time with materialized views. With dbt, we've enhanced testing of completeness, timeliness, and correctness of data we ingest on behalf of our customers. Our in-app analytics is as fast as any other application part, mainly thanks to optimized ClickHouse tables. We've achieved a lot not even a year into our journey with ClickHouse, and we're excited about the mindset of building modern cloud systems where analytics and operations are wholly integrated.

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