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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.

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Building StockHouse: Real-time market analytics with ClickHouse

Learn how we evolved our internal 2.1 PB data warehouse from traditional BI to an AI-first analytics platform

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How we made our internal data warehouse AI-first 

Learn about how failover-ready replication slots keep Postgres CDC pipelines running without interruption.

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ClickPipes for Postgres now supports failover replication slots

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.

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I've created a website to track the team's activity

ClickHouse 25.10 is available. In this post, you will learn about join improvements, a new data type for vector search, late materialization of secondary indices, and more!

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ClickHouse Release 25.10

Discover what’s new in ClickStack this month, including event deltas, multiline search, enhanced filtering, and more! 

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What's new in ClickStack. October '25.

Is it possible to derive global weather data from airplanes telemetry?

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Global weather data from flying airplanes

Learn how to implement log clustering with Drain3 and ClickHouse UDFs to automatically structure raw application logs. 

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 Improve logs compression with log clustering

We added QBit to ClickHouse, a column type that stores floats as bit planes. It lets you choose how many bits to read during vector search, tuning recall and performance without changing the data.

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We built a vector search engine that lets you choose precision at query time

A walkthrough showing how to trace an OpenAI agents app.

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Tracing OpenAI agents with ClickStack

Learn how giving logs a structured format can unlock 170x compression and make large-scale log storage practical.

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Compressing nginx logs 170x with column storage

Can open table formats like Iceberg and Delta Lake really power observability at scale, and if not, what is missing today and which innovations could unlock low cost open observability?

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Are open-table-formats + lakehouses the future of observability?

ClickHouse Cloud can now query Iceberg and Delta Lake tables directly through the DataLakeCatalog engine. Connect to Glue or Unity catalogs, discover tables automatically, and query your Lakehouse data instantly, all at ClickHouse speed.

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If it’s in your catalog, you can query it: The DataLakeCatalog engine in ClickHouse Cloud

The latest changes to the JSON data type in v25.8 make ClickHouse the leader in analytics over JSON data.

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Making complex JSON 58x faster, use 3,300x less memory, in ClickHouse

ClickHouse 25.9 is available. In this post, you will learn about new features, including a new text index, join reordering, streaming for secondary indices, and more.

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ClickHouse Release 25.9

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.

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Drinking the OTel SODA: Send Observability Data Anywhere

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