Filter By "Apache Druid"

Apache Druid uses load rules to manage the ageing of segments from one historical tier to another and finally to purge old segments from the cluster. In this article, we’ll show what happens when you make a mistake on the load rules which results in the premature removal of segments from the cluster. We’ll also show how to correct the mistake and recover the segments before they are permanently deleted.

This blog will help you understand what Elasticsearch and Druid do well and will help you decide whether you need one or both to reach your goals

The objective of this blog is to provide a step-by-step guide on setting up Druid locally, including the use of SQL ingestion for importing data and executing analytical queries.

Read on to learn more about common questions and answers about using Kafka with Druid.

Tales at Scale cracks open the world of analytics projects and shares stories from developers and engineers who are building analytics applications or working within the real-time data space. One of the key areas of focus for the show is Apache Druid.

Apache Druid uses partitioning (splitting data) and pruning (selecting subset of data) to achieve its legendary performance. Learn how to use the CLUSTERED BY clause during ingestion for performance and high concurrency.

Learn how to set up a pipeline that generates a simulated clickstream event stream and sends it to Confluent Cloud, processes the raw clickstream data using managed ksqlDB in Confluent Cloud, delivers the processed stream using Confluent Cloud, ingests these JSON events, using a native connection, into Imply Polaris and visualizes the event data in a dashboard.

Apache Druid is at the heart of Imply. We’re an open source business, and that’s why we’re committed to making Druid the best open source database for modern analytics applications

For an analytics app to handle real-time, streaming sources, it must be built for streaming data. Druid has 3 essential features for stream data.