How to use Azure Data Explorer for large-scale data analysis

One of the big issues facing anyone building a data-driven devops practice is, quite simply, the scale of the data you’re collecting. Logs from millions of users quickly add up, and the same is true of the internet of things or any other large source of data.

It’s a world where you’re generating terabytes of data from which you need to understand quickly what that data is telling you. Traditional databases aren’t much help, because you have to run that data through an extract, transform, load (ETL) process before you can start to explore it, even if you’re considering using data warehouse-style analytics tools.

Author: Simon Bisson

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