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

Understanding various architecture and components

Azure provides various data services that can be used to perform real-time analytics in different ways. In this section, we will learn about two different architectures and how different components are stitched together in both of these architectures to deliver the end result.

There are various use cases for real-time analytics, including the following:

  • Anomaly detection: This technique is used to identify unusual behavior or patterns that raises suspicions because of a significant difference from the rest of the data.
  • Supply chain analytics: This process is used to increase operational effectiveness by using data and quantitative methods for decision making.
  • Real-time personalization: This technique is used to gather information about the user visiting your website and engage that user by providing tailored content on the website based on their company, location, digital behavior, and so on.

The architecture...