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Azure Synapse Analytics Cookbook

You're reading from   Azure Synapse Analytics Cookbook Implement a limitless analytical platform using effective recipes for Azure Synapse

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781803231501
Length 238 pages
Edition 1st Edition
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Authors (2):
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Gaurav Agarwal(BLR) Gaurav Agarwal(BLR)
Author Profile Icon Gaurav Agarwal(BLR)
Gaurav Agarwal(BLR)
Meenakshi Muralidharan Meenakshi Muralidharan
Author Profile Icon Meenakshi Muralidharan
Meenakshi Muralidharan
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Toc

Table of Contents (11) Chapters Close

Preface 1. Chapter 1: Choosing the Optimal Method for Loading Data to Synapse 2. Chapter 2: Creating Robust Data Pipelines and Data Transformation FREE CHAPTER 3. Chapter 3: Processing Data Optimally across Multiple Nodes 4. Chapter 4: Engineering Real-Time Analytics with Azure Synapse Link Using Cosmos DB 5. Chapter 5: Data Transformation and Processing with Synapse Notebooks 6. Chapter 6: Enriching Data Using the Azure ML AutoML Regression Model 7. Chapter 7: Visualizing and Reporting Petabytes of Data 8. Chapter 8: Data Cataloging and Governance 9. Chapter 9: MPP Platform Migration to Synapse 10. Other Books You May Enjoy

Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Cloud Scale Analytics with Azure Data Services

Patrik Borosch

ISBN: 978-1-80056-293-6

  • Implement data governance with Azure services
  • Use integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure Monitor
  • Explore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wrangling
  • Implement networking with Synapse Analytics and Spark pools
  • Create and run Spark jobs with Databricks clusters
  • Implement streaming using Azure Functions, a serverless runtime environment on Azure
  • Explore the predefined ML services in Azure and use them in your app

Limitless Analytics with Azure Synapse

Prashant Kumar Mishra

ISBN: 978-1-80020-565-9

  • Explore the necessary considerations for data ingestion and orchestration while building analytical pipelines
  • Understand...
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