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)
1
Section 1: Introduction to Azure Data Explorer
5
Section 2: Querying and Visualizing Your Data
11
Section 3: Advanced Azure Data Explorer Topics

Chapter 11: Performance Tuning in Azure Data Explorer

Azure Data Explorer (ADX) is designed for high performance without the need for performance maintenance activities. However, it can still experience slow performance when overwhelmed by the workload. Therefore, it is important to understand performance tuning to ensure we maintain the high performance we know ADX delivers. In the examples we have seen so far, we have not had to worry about performance. Our datasets have been relatively small and even with the larger datasets we used on the help cluster, performance has not been an issue. With that said, as you make your cluster available to end users so that they can run queries and generate reports, their usage patterns and the queries they write can collectively impact performance.

In this chapter, we will begin by introducing performance tuning. Then, we will introduce workload groups, learn how they work, and how they can help preserve cluster performance. We will also create...