Book Image

Azure Synapse Analytics Cookbook

By : Gaurav Agarwal, Meenakshi Muralidharan
Book Image

Azure Synapse Analytics Cookbook

By: Gaurav Agarwal, Meenakshi Muralidharan

Overview of this book

As data warehouse management becomes increasingly integral to successful organizations, choosing and running the right solution is more important than ever. Microsoft Azure Synapse is an enterprise-grade, cloud-based data warehousing platform, and this book holds the key to using Synapse to its full potential. If you want the skills and confidence to create a robust enterprise analytical platform, this cookbook is a great place to start. You'll learn and execute enterprise-level deployments on medium-to-large data platforms. Using the step-by-step recipes and accompanying theory covered in this book, you'll understand how to integrate various services with Synapse to make it a robust solution for all your data needs. Whether you're new to Azure Synapse or just getting started, you'll find the instructions you need to solve any problem you may face, including using Azure services for data visualization as well as for artificial intelligence (AI) and machine learning (ML) solutions. By the end of this Azure book, you'll have the skills you need to implement an enterprise-grade analytical platform, enabling your organization to explore and manage heterogeneous data workloads and employ various data integration services to solve real-time industry problems.
Table of Contents (11 chapters)

Chapter 7: Visualizing and Reporting Petabytes of Data

In this chapter, we will cover how to visualize data and create reports using Power BI. You will be learning how to develop reports within a Synapse workspace and link Power BI to an existing dataset.

You will also learn something very interesting – how to integrate existing Power BI reports into the Synapse workspace and visualize the data in the same place without any difficulty.

Apart from this, you will also learn performance best practices for developing your Power BI reports with a very large dataset.

We will be covering the following recipes:

  • Combining Power BI and a serverless SQL pool
  • Working on a composite model
  • Using materialized views to improve performance