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

Understanding the application of ML

Today, ML is widely used in every industry to help companies and non-profit organizations gain deeper insights into their business through data. As a branch of artificial intelligence, it allows you to apply statistics with historical data to predict outcomes, forecast future values, detect anomalies in patterns, and more.

Some of the most popular techniques or algorithms used in ML are listed as follows:

  • Regression: This helps you respond to questions such as how much or how many. For example, how many units will I sell next quarter? How much crime will a region have next month? What is the projected fuel consumption given certain variables?
  • Classification: This helps you categorize events or data points. For example, will this customer churn (yes or no)? What animal is this?
  • Clustering: This helps you group data points together. For example, which customers tend to purchase this product? Which demographics are likely to engage...