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Limitless Analytics with Azure Synapse

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

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Product type Paperback
Published in Jun 2021
Publisher Packt
ISBN-13 9781800205659
Length 392 pages
Edition 1st Edition
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Authors (2):
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Saranya Ravichander Saranya Ravichander
Author Profile Icon Saranya Ravichander
Saranya Ravichander
Prashant Kumar Mishra Prashant Kumar Mishra
Author Profile Icon Prashant Kumar Mishra
Prashant Kumar Mishra
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Toc

Table of Contents (20) Chapters Close

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

Summary

In this chapter, we covered Azure Synapse Link, which is a new feature added to Azure Synapse, and we learned a step-by-step process to query data directly from an Azure Cosmos DB account. This feature dispenses with the need for ETL processes to bring data from a Cosmos DB account to Synapse. Now, we know that we can write queries directly on Cosmos DB data by creating corresponding linked services. We also saw how the transactional store syncs the data in the analytical store through auto-sync, and we learned about modes of schema representation in the analytical store. We used the Python language in this chapter; however, you are free to use any supported language that you are comfortable with.

There are many possible use cases of Azure Synapse Link. You can find a couple of these use cases mentioned in Microsoft Docs: https://docs.microsoft.com/en-us/azure/cosmos-db/synapse-link-use-cases.

In the next chapter, we are going to get some good coding experience on Azure...

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