Synapse Real-Time Analytics: Get started with KQL
Episode 8 of Fabric Espresso, the Microsoft Fabric video series created by Dr. Estera Kot.
About this episode
Text from the YouTube description.
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
11:13 `let` statements to set a variable name equal to an expression, and `toscalar` function
12:52 Wrap-Up
#microsoftfabric #synapserealtimeanalytics
Chapters
- 0:00 <Untitled Chapter 1>
- 0:06 Intro
- 0:17 Overview of Real-Time Analytics in Fabric
- 0:40 Running pre-defined queries
- 1:25 SQL to KQL using the `explain` keyword
- 2:23 Articles to get you started in KQL
- 2:39 `count` operator to count records in a table
- 3:02 `take` operator to get a few arbitrary records
- 4:01 `summarize` operator to aggregate data (e.g. find the min and max values in a column)
- 4:52 `where` operator to filter records
- 6:01 `top` operator to get the first N records sorted by the specified columns
- 6:38 `as` operator to bind a name to the operator's input tabular expression, and `extend` operator
- 7:51 `project` operator to select the columns to include in the output
- 8:13 `parse` operator to parse values into one or more calculated columns
- 9:05 `parse_json` function to extract values from JSONs
- 11:13 `let` statements to set a variable name equal to an expression, and `toscalar` function
- 12:52 Wrap-Up