In this lesson, we learn about the term operating analytics, and how it differs from traditional analytics. At the same time, we will look at the three most popular market solutions - Splunk, Azure Data Explorer and Kusto.

In short, such systems and solutions are not the main for the data engineer or BI engineer. For BI engineer, operating analytics is about another source of data that you will have to work with.

And for the data engineer, the solutions of operating analytics can be useful for many reasons, we can collect machine data (logs) about the work of our Data Pipelines, ETL, BIG DATA, etc., we can take data from operating analytics solutions and load data or data storage or data lake. And sometimes, we are asked to create NOSQL data solution based on Elastic Stack. (I was never asked, but suddenly!)

In this video you will learn:

- What is operating analytics and its role in BI/DW/BigData solutions

- Fundamentals and history of Splunk- About Azure Data Explorer and Kusto

- About Elastic Stack

- the main cases of using operating analytics and examples from experience

In laboratory work, I will show how to get Splunk, Adx and Elasticsearch.

Additional materials:

- [Data Learn webinar about Azure Data Explorer] ()

- [Elastic Search Tutorial] (

- [Splunk Tutorial] ()

- [Splunk leaves Russia (completely)] ()

- [year without Splunk - how the American company changed the market for machine data analysts in the Russian Federation and who left behind] ()

- [Splunk - a general description of the platform, basic features of the installation and architecture] ()

-[Quickstart: Create an Azure Data Explorer Cluster and Database] ()

- [1.lastic stack: analysis of security logs. Introduction] ()

- [2. Elastic Stack: Seciation analysis of logs. Logstash] ()

- [3. Elastic Stack: Seciation analysis of logs. Dashboards] ()

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