Azure Kusto Query Language Kql For Log Analytics And Fabric

Azure Kusto Query Language | KQL | Databricks Accounts, workspace, Notebook, job, spark logs

What you'll learn

Azure Kusto Query Language KQL

Azure Log Analytics

Azure Workbook

Microsoft Azure Fabric KQL

Requirements

Basic understanding of Azure Data Engineer services like Storage account, databricks

Description

After completion of this you would be writing Azure Kusto Query Language KQL comfortably using the Azure Log analytics and implement the Log Analytics workbook with metrics related to the import azure service logs from Databricks, spark, Azure logs.Kusto Query Language (KQL) is a powerful tool to explore your data and discover patterns, identify anomalies and outliers, create statistical modeling, and more. KQL is a simple yet powerful language to query structured, semi-structured, and unstructured data. The language is expressive, easy to read and understand the query intent, and optimized for authoring experiences. Kusto Query Language is optimal for querying telemetry, metrics, and logs with deep support for text search and parsing, time-series operators and functions, analytics and aggregation, geospatial, vector similarity searches, and many other language constructs that provide the most optimal language for data analysis. The query uses schema entities that are organized in a hierarchy similar to SQLs: databases, tables, and columns.KQL (Kusto Query Language) was developed with certain key principals in mind, like – easy to read and understand syntax, provide high-performance through scaling, and the one that can transition smoothly from simple to complex query.Interestingly KQL is a read-only query language, which processes the data and returns results. It is very similar to SQL with a sequence of statements, where the statements are modeled as a flow of tabular data output from the previous statement to the next statement. These statements are concatenated with a pipe (|) character.In SQL, the queries start with the column names and we only get to know about the table name when we reach the “From” statement, whereas, in KQL, the query starts with the table name followed by the pipe character after which the conditions are defined. We will see how this works shortly.

Overview

Section 1: Introduction

Lecture 1 Introduction to Azure Kusto Query Language KQL

Lecture 2 KQL Project function operator

Lecture 3 KQL Extend operator

Lecture 4 KQL Split and Json parsing

Lecture 5 KQL aggregation functions sum and count

Lecture 6 KQL Final Query hands on

Lecture 7 Databricks Account logs using the KQL

Lecture 8 Databricks workspace logs using the KQL

Lecture 9 Databricks Notebook logs using the KQL

Lecture 10 Databricks Cluster logs using the KQL

Lecture 11 Databricks job logs using the KQL

Lecture 12 Databricks Unity catalog and Spark logs using the KQL

Any Data engineer/Analyst who is working on Azure services for building Kusto Query language KQL queries,Who want to create a unified Azure workbook dashboard with azure service logs using the Kusto Query language KQL