Reasons to use Elasticsearch as an OLAP Cube

Question

At first glance, it seems that with Elasticsearch as a backend, it’s easy and quick to create reports with the ability to rotate, as in traditional business intelligence environments.

By pivot-like, I mean that in SQL terms, data is grouped from one to two dimensions, filtered, ordered by one or two dimensions, and aggregated by several labels, for example. with an amount or an invoice.

By “light”, I mean that with a sufficiently large cluster, preliminary data aggregation is not required, which saves ETL and data development time.

By “fast,” I mean that because of Elasticsearch's near real-time availability latency, it can be reduced in many cases compared to traditional business intelligence systems.

Are there any reasons rather than using Elasticsearch for the above purpose?

Answer 1

ElasticSearch is a great alternative to the cube, we use it for the same purpose today. One huge advantage is that with a cube you need to know what dimensions you want to create. With ES, you just push more and more data and figure out how you want to report it.

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