This is great news for the broader community, even if you do not have a use for Druid per se.
Database engines designed for real-time analytics have a significantly different internal structure than either traditional OLTP or popular analytical systems like Hadoop. Most people just try to (badly) fit real-time analytic workloads into a database engine not designed for it. Druid is the first open source example I am aware of that has internals designed for these types of workloads.
Few software engineers know what the inside of a real-time analytical database looks like. This will provide a great starting point. (The only major missing component is the non-trivial, custom I/O scheduling engine required to back these engines to disk instead of in-memory.)
Comments
This is great news for the broader community, even if you do not have a use for Druid per se.
Database engines designed for real-time analytics have a significantly different internal structure than either traditional OLTP or popular analytical systems like Hadoop. Most people just try to (badly) fit real-time analytic workloads into a database engine not designed for it. Druid is the first open source example I am aware of that has internals designed for these types of workloads.
Few software engineers know what the inside of a real-time analytical database looks like. This will provide a great starting point. (The only major missing component is the non-trivial, custom I/O scheduling engine required to back these engines to disk instead of in-memory.)