You're reading from Limitless Analytics with Azure Synapse An end-to-end analytics service for data processing, management, and ingestion for BI and ML

Product type Paperback

Published in Jun 2021

Publisher Packt

ISBN-13 9781800205659

Length 392 pages

Edition 1st Edition

Languages

Python

Tools

Azure

Concepts

Data Processing

Authors (2):

Saranya Ravichander

Prashant Kumar Mishra

View More author details

Table of Contents (20) Chapters

Preface

1. Section 1: The Basics and Key Concepts

2. Chapter 1: Introduction to Azure Synapse FREE CHAPTER

3. Chapter 2: Considerations for Your Compute Environment

4. Section 2: Data Ingestion and Orchestration

5. Chapter 3: Bringing Your Data to Azure Synapse

6. Chapter 4: Using Synapse Pipelines to Orchestrate Your Data

7. Chapter 5: Using Synapse Link with Azure Cosmos DB

8. Section 3: Azure Synapse for Data Scientists and Business Analysts

9. Chapter 6: Working with T-SQL in Azure Synapse

10. Chapter 7: Working with R, Python, Scala, .NET, and Spark SQL in Azure Synapse

11. Chapter 8: Integrating a Power BI Workspace with Azure Synapse

12. Chapter 9: Perform Real-Time Analytics on Streaming Data

13. Chapter 10: Generate Powerful Insights on Azure Synapse Using Azure ML

14. Section 4: Best Practices

15. Chapter 11: Performing Backup and Restore in Azure Synapse Analytics

16. Chapter 12: Securing Data on Azure Synapse

17. Chapter 13: Managing and Monitoring Synapse Workloads

18. Chapter 14: Coding Best Practices

19. Other Books You May Enjoy

Querying the Cosmos DB analytical store

With Azure Synapse, you get the option to choose between Spark or SQL as your compute environment. You can query a Cosmos DB analytical store using Spark and SQL Serverless, however, this feature is not available with SQL provisioned as of now.

Let's learn how to query data in the analytical store of a Cosmos DB container.

Querying with Azure Synapse Spark

Azure Synapse Spark allows you to analyze data in your Synapse Link enabled Azure Cosmos DB containers. You can query an analytical store from Spark in two possible ways:

Loading data to a Spark DataFrame
Creating a Spark table

A Spark DataFrame leverages the cached metadata through the lifetime of the Spark session, so any change in the source data will not be reflected here until you start a new Spark session. The metadata of the analytical store is reloaded on every query execution against the Spark table.

You can ingest data to the analytical store of...