Book Image

Learn Azure Synapse Data Explorer

By : Pericles (Peri) Rocha
Book Image

Learn Azure Synapse Data Explorer

By: Pericles (Peri) Rocha

Overview of this book

Large volumes of data are generated daily from applications, websites, IoT devices, and other free-text, semi-structured data sources. Azure Synapse Data Explorer helps you collect, store, and analyze such data, and work with other analytical engines, such as Apache Spark, to develop advanced data science projects and maximize the value you extract from data. This book offers a comprehensive view of Azure Synapse Data Explorer, exploring not only the core scenarios of Data Explorer but also how it integrates within Azure Synapse. From data ingestion to data visualization and advanced analytics, you’ll learn to take an end-to-end approach to maximize the value of unstructured data and drive powerful insights using data science capabilities. With real-world usage scenarios, you’ll discover how to identify key projects where Azure Synapse Data Explorer can help you achieve your business goals. Throughout the chapters, you'll also find out how to manage big data as part of a software as a service (SaaS) platform, as well as tune, secure, and serve data to end users. By the end of this book, you’ll have mastered the big data life cycle and you'll be able to implement advanced analytical scenarios from raw telemetry and log data.
Table of Contents (19 chapters)
1
Part 1 Introduction to Azure Synapse Data Explorer
6
Part 2 Working with Data
12
Part 3 Managing Azure Synapse Data Explorer

Tuning and Resource Management

As organizations build analytical environments and grow a data culture among their employees, more and more individuals will consume data to support their decision-making process, to carry out experiments, to analyze product data, or for any needs that can be resolved with a query to the source data. While the number of users in your analytical environment grows, remember that your compute resources are not unlimited. You have to find ways to govern how many compute resources each user gets to avoid exhausting them or losing control of your costs.

In this chapter, you will learn about ways to reserve resource usage based on the properties of the incoming request, such as who submitted the request, what application they are using, and more. This helps you prioritize how resources are used in the cluster and offer predictable performance for all users. You’ll also learn how to queue requests for delayed execution when your compute cluster reaches...