Filter By "druid"

This blog is focused on how Imply Pivot delivers the final mile for building an anlaytics app. It showcases two customer examples - Twitch and ironsource.

Imply Pivot offers multi step aggregations, which is valuable for timeseries data where measures are not evenly distributed in time.

We obviously talk a lot about #ApacheDruid on here. But what are folks actually building with Druid? What is a modern analytics application, exactly? Let's find out

Elasticity is important, but beware the database that can only save you money when your application is not in use. The best solution will have excellent price-performance under all conditions.

Many of Druid’s improvements focus on building a solid foundation, including making the system more stable, easier to use, faster to scale, and better integrated with the rest of the data ecosystem. But for advanced users and potential/existing contributors of the Druid project, here's a peek behind the scenes.

Apache Druid 0.23.0 contains over 450 updates, including new features, major performance enhancements, bug fixes, and major documentation improvements.

There can’t be one database good at everything. When it comes to real-time analytics, you need a database built for it.

Lean the basics of Public Key Infrastructure (PKI) as it relates to Druid and Zookeeper security.

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user stories, define an asynchronous download API, and deliver it in a monthly STS release.