Quick overview of Azure Data Explorer

A big data analytics platform called Azure Data Explorer makes it simple to quickly evaluate large amounts of data. You may get a complete end-to-end solution for data ingestion, query, visualization, and administration with the Azure Data Explorer toolset.

Azure Data Explorer makes it simple to extract critical insights, identify patterns and trends, and develop forecasting models by analyzing structured, semi-structured, and unstructured data over time series. Azure Data Explorer is helpful for log analytics, time series analytics, IoT, and general-purpose exploratory analytics. It is completely managed, scalable, secure, resilient, and enterprise ready.

Terabytes of data may be ingested by Azure Data Explorer in minutes in batch or streaming mode, and petabytes of data can be searched with millisecond response times. Data can be ingested in a variety of structures and formats. It may enter from a number of channels and sources.

The Kusto Query Language (KQL), an open-source language created by the team, is used by Azure Data Explorer. The language is straightforward to comprehend and learn, and it is quite effective. Both basic operators and sophisticated analyses are available. Microsoft makes extensive use of it (Azure Monitor – Log Analytics and Application Insights, Microsoft Sentinel, and Microsoft 365 Defender). KQL is designed with quick, varied big data exploration in mind. queries any other tabular expressions, including views, functions, and tables. This can involve clusters or even tables from various databases.

Each table’s data is kept as data shards, commonly referred to as “extents.” Based on the ingestion time, all data is automatically indexed and partitioned. There are no main foreign key requirements or other restrictions, like as uniqueness, unlike a relational database. As a result, you may store a wide variety of data and quickly query it thanks to the way it is kept.