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 data loading process

In Chapter 4, Real-World Usage Scenarios, we discussed sample architectures that you can use as blueprints in your own projects. For every single one of them, on the left-hand side of the diagrams, you could see data sources, followed by the Ingest stage. Your data load strategy, and the services you will use to perform data ingestion, will depend on your data source types and your latency requirements.

Simply put, the data loading process can be summarized into four steps:

  1. Defining a retention policy.
  2. Choosing a data load strategy.
  3. Creating destination tables and data mappings.
  4. Performing data ingestion.

Note that before you perform the data ingestion task, you should create your destination tables and data mappings. Since we will explore different ways to perform data ingestion, the steps to create destination tables and data mappings in this chapter (when needed) will be presented in topics where we will perform...