To meet critical requirements, the Confluence Analytics Experience Team chose to deploy Imply Enterprise Hybrid, a complete, real-time database built from Apache Druid® that runs in Atlassian’s VPC with Imply’s management control plane.
A stack for real-time analytics applications.
Imply Pivot offers multi step aggregations, which is valuable for timeseries data where measures are not evenly distributed in time.
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A look at what can be done with Druid for upserts and data deduplication.
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.
This is part two of the Druid 0.23.0 release blog. 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.
Apache Druid 0.23.0 contains over 450 updates from 81 contributors, including new features, significant 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.
