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

Introducing performance tuning

Before we jump into workload groups, let's spend a few moments thinking about performance tuning in general. In general, performance should not be an issue, given that ADX has been designed and optimized to be a big data service that is highly scalable and fast. As you ingest more and more data and allow more users and applications to query your clusters, you may experience some performance degradation. Therefore, it is important to beware of performance tuning concepts and what features ADX provides to help tune performance when the time comes.

Like troubleshooting, which we discussed in Chapter 9, Monitoring and Troubleshooting Azure Data Explorer, performance tuning can be considered as a process. The goal of performance tuning is to identify bottlenecks, troubleshoot their causes, and apply the features that are available to us, such as workload groups, cache policies, and so on, to eliminate bottlenecks. It is also important to understand...