Note: This page is deprecated. For all documentation, please see druid.io/docs/latest
Druid is an open-source analytics datastore designed for realtime, exploratory, queries on large-scale data sets (100’s of Billions entries, 100’s TB data). Druid provides for cost effective, always-on, realtime data ingestion and arbitrary data exploration.
Druid was originally created to resolve query latency issues seen with trying to use Hadoop to power an interactive service. Hadoop has shown the world that it’s possible to house your data warehouse on commodity hardware for a fraction of the price of typical solutions. As people adopt Hadoop for their data warehousing needs, they find two things.
The first one is the joy that everyone feels the first time they get Hadoop running. The latter is what they realize after they have used Hadoop interactively for a while because Hadoop is optimized for throughput, not latency. Druid is a system that you can set up in your organization next to Hadoop. It provides the ability to access your data in an interactive slice-and-dice fashion. It trades off some query flexibility and takes over the storage format in order to provide the speed.
Druid is especially useful if you are summarizing your data sets and then querying the summarizations. If you put your summarizations into Druid, you will get quick queryability out of a system that you can be confident will scale up as your data volumes increase. Deployments have scaled up to 2TB of data per hour at peak ingested and aggregated in real-time.
The data store world is vast, confusing and constantly in flux. This page is meant to help potential evaluators decide whether Druid is a good fit for the problem one needs to solve. If anything about it is incorrect please provide that feedback on the mailing list or via some other means, we will fix this page.
Last edited by fjy,