Azure Data is Explorer (ADX) is a service designed to analyze large amounts of structured and semi-structured data in near real-time. It was developed in 2015 for use by internal Microsoft teams but was used in external products and generally released in 2019.

ADX has several advantages:

  • Because ADX is designed for insertions and not for updates, inserting data is very fast, making it ideal for processes, such as IoT applications or Logging that need to absorb data frequently. An absence of database constraints, such as unique primary keys and parent-child relationships make insertions very fast.
  • ADX can store massive amounts of data. Internally, Microsoft uses ADX to store huge sets of data, such as Microsoft Office Client telemetry data and Azure Activity Logs.
  • More and/or larger servers can be added to an ADX cluster to meet increased workloads by scaling up or out. Clusters can be scaled back down when workloads decrease to reduce costs. Servers in an ADX Cluster are automatically synchronized. There are even autoscale options to automatically add and remove servers based on the workload.
  • Because ADX is a managed service, the low-level tasks of managing the servers and other infrastructure are abstracted away from you, so you can focus on the data you are collecting and analyzing.

ADX ships with the Kusto Query Language (KQL) - a declarative language that allows you to not only sort, filter, and return columns, but is ideal for querying time series data. KQL also includes a number of useful functions to perform tasks, such as analyzing geographic data and visualizing data. The Azure Portal allows an interface to manage clusters and execute queries, but KQL queries can also be integrated into reporting tools, such as Power BI and Tableau.

KQL is a read-only query language, so there is no risk of data corruption through injection attacks.

ADX is capable of quickly returning results from very large data sets.

This article provides informatio on ADX and how it is able to achieve its speed and scalability.

In the next few articles, I will show you how to get up and running using Azure Data Explorer.