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

Building Machine Learning Experiments

By now, you’ve learned how to load data into your Data Explorer pools and perform analysis using the Kusto Query Language (KQL), Python/PySpark, and Power BI. The next boundary to explore in analytics is how to do a more comprehensive analysis of your data, and even predict future behavior. Machine Learning (ML) helps us cross this new boundary, and Azure Synapse offers a vast array of options to apply ML algorithms to your data. In this chapter, you will learn how to build ML experiments with your Data Explorer pool.

First, you will learn how ML is applied to day-to-day tasks by discussing the most common algorithms that are used today. Note that this will not be an extensive list of all the ML algorithms, but it will help you understand the core tasks that it helps resolve.

Next, you will train a new model using Automated Machine Learning (AutoML). You will create a new Azure Machine Learning workspace and link it to your Azure Synapse...