Book Image

Limitless Analytics with Azure Synapse

By : Prashant Kumar Mishra
Book Image

Limitless Analytics with Azure Synapse

By: Prashant Kumar Mishra

Overview of this book

Azure Synapse Analytics, which Microsoft describes as the next evolution of Azure SQL Data Warehouse, is a limitless analytics service that brings enterprise data warehousing and big data analytics together. With this book, you'll learn how to discover insights from your data effectively using this platform. The book starts with an overview of Azure Synapse Analytics, its architecture, and how it can be used to improve business intelligence and machine learning capabilities. Next, you'll go on to choose and set up the correct environment for your business problem. You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. Later, you'll explore how to handle both relational and non-relational data using the SQL language. As you progress, you'll perform real-time streaming and execute data analysis operations on your data using various languages, before going on to apply ML techniques to derive accurate and granular insights from data. Finally, you'll discover how to protect sensitive data in real time by using security and privacy features. By the end of this Azure book, you'll be able to build end-to-end analytics solutions while focusing on data prep, data management, data warehousing, and AI tasks.
Table of Contents (20 chapters)
Section 1: The Basics and Key Concepts
Section 2: Data Ingestion and Orchestration
Section 3: Azure Synapse for Data Scientists and Business Analysts
Section 4: Best Practices


This chapter was primarily focused on Synapse SQL. We learned different T-SQL language elements that are supported in Synapse SQL, as well as their limitations. We learned how we can use T-SQL statements with structured, semi-structured, or unstructured data. In this chapter, we also covered how to manage transactions efficiently to avoid any transaction failures. We also learned that we could create stored procedures and views in Synapse SQL in a similar way to how we do this in SQL Server. Synapse SQL provides a few additional features to read data directly from a data lake.

We saw some of the system views supported in Synapse SQL. We also learned how to use sample scripts to build our logic as per the business need.

The next chapter will be more focused on Synapse Spark, where we will learn how to write code in different languages in Synapse Spark without worrying about infrastructure management. We will also learn how to use notebooks in Synapse Studio.