Synapse Real-Time Analytics: Get started with KQL

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Welcome to the third video in our How To series for Real-Time Analytics in Microsoft Fabric! In this video, Slavik Neimer, a Principal Software Engineer Manager

Synapse Real-Time Analytics: Get started with KQL Welcome to the third video in our How To series for Real-Time Analytics in Microsoft Fabric! In this video, Slavik Neimer, a Principal Software Engineer Manager at Microsoft, will show you how to get started with KQL.

00:06 Intro
00:17 Overview of Real-Time Analytics in Fabric
00:40 Running pre-defined queries
01:25 SQL to KQL using the explain keyword
02:23 Articles to get you started in KQL
02:39 count operator to count records in a table
03:02 take operator to get a few arbitrary records
04:01 summarize operator to aggregate data (e.g. find the min and max values in a column)
04:52 where operator to filter records
06:01 top operator to get the first N records sorted by the specified columns
06:38 as operator to bind a name to the operator's input tabular expression, and extend operator
07:51 project operator to select the columns to include in the output
08:13 parse operator to parse values into one or more calculated columns
09:05 parse_json function to extract values from JSONs

KQL stands for Kusto Query Language. It is a read-only request to process data and return results. The language is highly expressive and is used for complex data exploration that includes many steps. It is primarily used for querying large datasets in Azure Data Explorer (ADX) and the backend of several Microsoft Azure services.

Azure Data Explorer, also known as Kusto, is a fast and scalable data exploration service for analyzing large volumes of diverse data from various sources, such as applications, websites, IoT devices, and more. KQL, being the default language for ADX, is used to explore, analyze, and visualize data.

It's also important to mention that KQL is used extensively in Microsoft security and compliance tools, like Azure Sentinel and Microsoft 365 Defender.

Deep Dive into KQL in Synapse Real-Time Analytics

KQL (Kusto Query Language) is the language used in Microsoft Synapse Real-Time Analytics to query and analyze data. This video guides you through an introduction to KQL and its operators. By learning to use operators like count, take, summarize, where, top, as, project, and parse, you can efficiently manipulate data and draw insights from it. KQL also supports parsing JSON objects, enabling you to work with complex data types. Mastering KQL is an essential skill in maximizing the capabilities of Synapse Real-Time Analytics and fully utilizing your data.

Learn about Synapse Real-Time Analytics: Get started with KQL

Synapse Real-Time Analytics is a powerful tool for collecting and analyzing data in real-time. In this video, Slavik Neimer, a Principal Software Engineer Manager at Microsoft, will show you how to get started with KQL (Kusto Query Language). He will discuss pre-defined queries, how to convert SQL to KQL, and the various other KQL operators and functions such as count, take, summarize, where, top, as, extend, project, parse, and parse_json. He will also demonstrate how to use each operator to manipulate and analyze data in real-time.

The count operator can be used to count the records in a table, while take can be used to get a few arbitrary records. The summarize operator can be used to aggregate data, such as finding the minimum and maximum values in a column. The where operator can be used to filter records, while the top operator can be used to get the first N records sorted by the specified columns. The as operator can be used to bind a name to the operator's input tabular expression, and the extend operator can be used to add new columns to a table. The project operator can be used to select the columns to include in the output, while the parse operator can be used to parse values into one or more calculated columns. Finally, the parse_json function can be used to extract values from JSONs.

Keywords

KQL, Real-Time Analytics, Fabric, Synapse, Aggregate Data

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