Apache Druid is known for its ability to handle schema changes during data ingestion and query in an extraordinarily flexible way. Normally, you don’t even need to worry because everything is handled automatically for you. However, there are a few edge cases that data engineers and data analysts should be aware of. Let’s have a look.

Apache Druid is a high performance, distributed OLAP database that also has characteristics of a timeseries database. One feature that sets Druid apart from its competitors is the ability to use multi-value dimensions. The documentation on this feature seems to be a bit terse though, so I am going to start a loose series of posts to shed some light on questions such as:

Imply Pivot has the built in capability to send scheduled email reports on a regular basis. However, a few things need to be configured correctly in order to make this work. Here is a comprehensive list of the required settings.

Druid’s native query language supports five distinct query types. Each of these query types requires that certain attributes be set in the query JSON object.

Financial transactions present a whole lot of interesting technology use cases.