We benchmarked the five major cloud data warehouses at 1B–100B rows using their real billing models to measure performance per dollar. Results show how cost-performance shifts as data grows.
This guide explains how the five major cloud data warehouses—Snowflake, Databricks, ClickHouse Cloud, BigQuery, and Redshift—allocate, meter, and bill compute, giving engineers a clear understanding of what the billing units mean and how to compare them.
In this post, we’ll explore why log analysis is inherently hard, what event patterns solve, how ClickStack implements them, and why we’ve chosen to do so at query time. We’ll also share practical guidance on how to make the most of event patterns today, a
“Moving to ClickHouse, we managed to get 70% cheaper costs and improved our search performance by 10x. We’re doing more and storing more with less.”
Read how Streamfold’s Rotel pushes OpenTelemetry 4x faster into ClickHouse - benchmarking efficiency at scale and revealing the tools that make it happen
“Our query efficiency has improved significantly since moving to ClickHouse. We can now show our clients real-time analytics.” - Sidhant Gaba, Engineer
How teams used ClickHouse to power agents with streaming data, sub‑second analytics, and production‑ready dashboards at the AWS MCP Hackathon.
ClickHouse today announced the availability of a powerful new integration with Microsoft OneLake, the unified data lake service within Microsoft Fabric.
We’re excited to announce a new integration between ClickHouse and Microsoft OneLake, powered by the OneLake Tables APIs and Apache Iceberg.
“Cost has gone down, speed has gone up, and the scale of our customers has increased exponentially.” Nir Barak, Principal Data Engineer
chDB recently upgraded the ClickHouse kernel from v25.5 to v25.8.2.29. This blog post goes through that journey.
Welcome to the November 2025 ClickHouse newsletter, which will round up what’s happened in real-time data warehouses over the last month.
Learn more how we built StockHouse, a real-time financial analytics application with Massive, ClickHouse and Perspective that scales to thousands of events per second.
“Even if data increases by 4x or 5x in the next year, we literally don't have to worry about it. We’ve got the architecture right, the stack right, and the right platform at the end of the day.” - Akshay Gupta, Lead Data Engineer
With community-standard instrumentation and the OTel Collector acting as a translation and routing engine, your metrics, logs, and traces are no longer trapped in a walled garden.
Learn how we evolved our internal 2.1 PB data warehouse from traditional BI to an AI-first analytics platform
Learn about how failover-ready replication slots keep Postgres CDC pipelines running without interruption.
“High-cardinality data is core to how we track function-level behavior across versions. ClickHouse stood out because it handled that complexity natively, while other systems made it harder to work at that level of granularity.”
Initially, I wanted to make a single report to incentivise for looking at and resolving GitHub issues. But I'm greedy, so in the end it appeared to be a website where you can compare various metrics on team activities across GitHub repositories.
Discover what’s new in ClickStack this month, including event deltas, multiline search, enhanced filtering, and more!