![]() Redpanda, however, provides a fast and safe-by-default system that’s API compatible with Kafka. Kafka is often suitable, but it might not be enough for some use cases that need high-performance and low-resource usage in particular. Streaming and processing data in real time requires high performance and low latency. You can stream any data from Kafka to a ClickHouse table with a few easy configurations, enabling your system to process data in real time. Kafka allows you to publish or subscribe to data flows, organize fault-tolerant storage, and process streams as they become available. It handles real-time data in a very efficient way and provides integrations with many NoSQL or relational databases and streaming platforms like RabbitMQ and Apache Kafka®. You can use ClickHouse to keep your platform logs or use it as your event store for your high-traffic business. This makes ClickHouse an excellent choice when it comes to processing hundreds of millions (or even over a billion) rows of data and tens of gigabytes of data per server, per second for analysis. On the other hand, OLAP databases that can scale and provide performant queries are becoming more popular every day.ĬlickHouse, is a scalable, reliable, secure, and performant OLAP database that works one hundred to one thousand times faster than traditional approaches. Relational databases are increasingly inefficient for analytical data-processing needs. All of this leads to greater resources and staffing needed to gather and analyze the data, especially if the company’s data processes are manual, whether in whole or in part. More raw data means more data to be analyzed, and this means more data output. ![]() Many businesses that capture and analyze huge amounts of data on a daily basis create even more data as they report on their findings.Īs these companies and their data continue to grow, real-time data analysis becomes more and more important.
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