SQL for large-scale Event Analytics
Did you ever whish you could insert a row into your SQL database for every page view? With EventQL, you can:
The columnar storage engine supports any schema - including arbitrary nesting within events - so you can store any JSON objects as rows. Internally, tables are split into partitions and distributed over many servers. Queries also run on many servers in parallel.
Given enough machines, or in an EventQL Cloud instance, you can query hundreds of terrabytes of event data at sub-second latency. This concept is sometimes referred to as "massively parallel database architecture".
Here are a few more example scenarios that are particularly well suited to EventQL's design:
- Streaming web tracking & analytics
- High volume event and sensor data logging
- Serverless real-time dashboards
Note that EventQL is built around specific design choices that make it an excellent fit for real-time data analytics processing (OLAP) tasks, but also mean it's not well suited for most transactional (OLTP) workloads.