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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

Understanding Spark pool

Apache Spark is a very fast unified analytics engine for big data and machine learning.

Synapse Spark Pool is one of Microsoft's implementations of Apache Spark in Azure. Synapse Analytics workspace has a Spark engine built in, along with Notebook support. Because Synapse Spark supports C#, we can write Spark .NET directly within notebooks. You can also write your code in Python, Scala, C#, and SQL.

One Spark pool can be accessed by multiple users, but for every user, one new Spark instance will be created. A Spark instance is also dependent on the Spark pool capacity: if there is enough capacity in the pool to run multiple queries, the existing instance will be able to process the job; otherwise, a new instance will be created to process the job.

The following diagram displays different components of Apache Spark on Azure Synapse:  

Figure 2.17 – Apache Spark in Azure Synapse Analytics

Figure 2.17 – Apache Spark in Azure Synapse Analytics

Let&apos...

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