ClickHouse is an open-source column-oriented database management system that provides high-speed querying and efficient processing of large-scale data for real-time applications and analytics.

ClickHouse

ClickHouse is an open-source, column-oriented OLAP database management system that allows users to generate analytical reports using SQL queries in real-time. Its technology works 100-1000x faster than traditional database management systems, and processes hundreds of millions to over a billion rows and tens of gigabytes of data per server per second. With a widespread user base around the globe, the technology has received praise for its reliability, ease of use, and fault tolerance. Learn more at clickhouse.com.

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Thrive Capital

Index Ventures

Yandex

Redpoint

FirstMark

Almaz Capital

Altimeter Capital

Lead Edge Capital

Benchmark

Lightspeed Venture Partners

Coatue

🎉Celebrating 1 year of ClickHouse Cloud! 🎂🥳

Simple, Serverless, Secure...ClickHouse.

Thanks to our users and customers for their belief in our vision and joining us on the journey of unbundling the cloud data warehouse.

https://lnkd.in/dVt43fnn

In the Seattle area? Love ClickHouse? Then come join us on 3/11 at the Statsig offices. We have amazing speakers from Twilio, Cognitiv and ClickHouse. Come and join us!

Version 24.2 optimizes memory usage for very large tables (when using petabytes of shared storage) and improves user experience by automatically detecting data formats.

Join us for the monthly release and community call next week 👇

At ClickHouse one of our top use cases is migrating away from Postgres, when it hits a tipping point for running analytical workloads. Curious if others are seeing this too?

It usually starts with a frantic inbound email or support case. Front-end application is down or very slow, because Postgres is used to power a real-time dashboard or some other real-time analytics application. It worked on small datasets, but as historical data grew and the number of concurrent users went up, suddenly queries started taking 10s of seconds and the application is now virtually unusable. 

Scaling up or out does not solve the problem fundamentally, because every query is scanning too many rows and the problem keeps getting worse faster than you can throw resources at it. Even if you could fix it that way, the costs would be astronomical. 

The remedy is migrating this workload from row-oriented OLTP database to column-oriented OLAP database (like ClickHouse). Thankfully, it is not that big of a lift (especially if you use ClickHouse Cloud), and typically within days the migration can be achieved.

🚀 Building a Change Data Capture (CDC) pipeline can be a challenging task and one we see our users undergoing frequently. And now, Streamkap has a solution for ClickHouse! Here's why you should check it out:
🛠️ Out-of-the-box solution: Streamkap offers a ready-made solution that performs seamlessly at scale.
🔍 Detailed challenges and solutions: They've meticulously documented the hurdles they encountered and how they overcame them, serving as a valuable resource for anyone facing similar issues.
💡 Whether you're tackling the same problem or simply curious about Streamkap, their CDC pipeline solution is worth exploring!
#Streamkap #ClickHouse #CDCpipeline #DataSolutions

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