Book Image

Scalable Data Analytics with Azure Data Explorer

By : Jason Myerscough
Book Image

Scalable Data Analytics with Azure Data Explorer

By: Jason Myerscough

Overview of this book

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.
Table of Contents (18 chapters)
Section 1: Introduction to Azure Data Explorer
Section 2: Querying and Visualizing Your Data
Section 3: Advanced Azure Data Explorer Topics


In this chapter, we started by introducing performance tuning and discovered that performance tuning is a process similar to troubleshooting. Next, we learned what workload groups are and how to configure them. We created an example workload group that restricted the number of requests that members from a specific AAD group can make. We discovered the three components that are required to use workload groups are the request classification policy, which is responsible for assigning requests to workload groups, the workload groups themselves, and the workload group policies that allow us to apply restrictions to requests, such as rate limiting.

Next, we discovered how to manage our cluster performance by managing the hot cache from the data plane. By managing the cache at the data plane, we can allow database administrators to tune performance, without giving them access to the management plane.

Next, we introduced the .show queries and .show commands KQL management commands...