Run sub-second SQL queries against a Hadoop data warehouse with billions of rows
Build a business intelligence dashboard backed by massive company datasets without waiting hours per query
Try out OLAP-style multi-dimensional analytics on big data using the provided Docker image before committing to a full cluster
The Docker image covers getting started quickly, but production use requires a running Hadoop cluster with distributed infrastructure.
Apache Kylin is an open source analytics engine originally contributed by eBay. It is designed to run SQL queries against very large datasets stored in Hadoop, a distributed data processing system used by many large companies to store and process massive amounts of information. The core idea is to let teams ask questions of their data using standard SQL, a query language that non-technical users sometimes encounter in spreadsheet tools or business intelligence software. Kylin pre-computes results across many combinations of dimensions so that queries that would otherwise take hours come back quickly. Getting started is straightforward. The project provides a ready-made Docker image, which is a self-contained package you can run on your own computer with two terminal commands. After a few minutes a web interface becomes available at a local URL where you can log in and begin exploring. The README is short and points to external documentation and a mailing list for support. It does not describe pricing, cloud hosting options, or any specific industries it targets. The project is released under the Apache 2.0 open source license.
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