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

Managing IoT data

As discussed in the When to use Azure Synapse Data Explorer section of Chapter 1, Introducing Azure Synapse Data Explorer, we learned that Data Explorer pools are optimized for unstructured data, which typical IoT sensors or devices generate. Companies that make use of sensors to monitor manufacturing processes, fleet management, and many other scenarios that work with sensor-generated data commonly deal with this sort of unstructured data. This section demonstrates a blueprint that describes how to manage big data in IoT scenarios.

This blueprint can be useful in scenarios such as the following ones:

  • Predictive maintenance: Using data from sensors, you can estimate when you should perform maintenance on factory equipment. This is a key strategy that’s adopted by companies to save on operating costs as they can perform maintenance at the optimal time in the life cycle of machines.
  • Fleet management: Vehicles of any type (cars, drones, boats, or...