Scalable Data Analytics with Azure Data Explorer: Modern ways to query, analyze, and perform real-time data analysis on large volumes of data. This item is unavailable.Scalable Data Analytics with Azure Data Explorer: Modern ways to query, analyze, and perform real-time data analysis on large volumes of data

Write efficient and powerful KQL queries to query and visualize your data and implement best practices to improve KQL execution performance

Key Features

• Apply Azure Data Explorer best practices to manage your data at scale and reduce KQL execution time
• Discover how to query and visualize your data using the powerful KQL
• Manage cluster performance and monthly costs by understanding how to size your ADX cluster correctly

Book Description

Azure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters.

The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance.

By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI.

What you will learn

• Become well-versed with the core features of the Azure Data Explorer architecture
• Discover how ADX can help manage your data at scale on Azure
• Get to grips with deploying your ADX environment and ingesting and analyzing your data
• Explore KQL and learn how to query your data
• Query and visualize your data using the ADX UI and Power BI
• Ingest structured and unstructured data types from an array of sources
• Understand how to deploy, scale, secure, and manage ADX

Who this book is for

This book is for data analysts, data engineers, and data scientists who are responsible for analyzing and querying their team's large volumes of data on Azure. SRE and DevOps engineers who deploy, maintain, and secure infrastructure will also find this book useful. Prior knowledge of Azure and basic data querying will help you to get the most out of this book.

Table of Contents

1. Introducing Azure Data Explorer
2. Building Your Azure Data Explorer Environment
3. Exploring the Azure Data Explorer UI
4. Ingesting Data in Azure Data Explorer
5. Introducing the Kusto Query Language
6. Introducing Time Series Analysis
7. Identifying Patterns, Anomalies, and Trends in Your Data
8. Data Visualization with Azure Data Explorer and Power BI
9. Monitoring and Troubleshooting Azure Data Explorer
10. Azure Data Explorer Security
11. Performance Tuning in Azure Data Explorer
12. Cost Management in Azure Data Explorer

Jason Myerscough is a director of Site Reliability Engineering and cloud architect at Nuance Communications. He has been working with Azure daily since 2015. He has migrated his company's flagship product to Azure and designed the environment to be secure and scalable across 16 different Azure regions by applying cloud best practices and governance. He is currently certified as an Azure Administrator (AZ-103) and an Azure DevOps Expert (AZ-400). He holds a first-class bachelor's degree with honors in software engineering and a first class master’s degree in computing.