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Cloud Scale Analytics with Azure Data Services

You're reading from   Cloud Scale Analytics with Azure Data Services Build modern data warehouses on Microsoft Azure

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800562936
Length 520 pages
Edition 1st Edition
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Author (1):
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Patrik Borosch Patrik Borosch
Author Profile Icon Patrik Borosch
Patrik Borosch
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Data Warehousing and Considerations Regarding Cloud Computing
2. Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses FREE CHAPTER 3. Chapter 2: Connecting Requirements and Technology 4. Section 2: The Storage Layer
5. Chapter 3: Understanding the Data Lake Storage Layer 6. Chapter 4: Understanding Synapse SQL Pools and SQL Options 7. Section 3: Cloud-Scale Data Integration and Data Transformation
8. Chapter 5: Integrating Data into Your Modern Data Warehouse 9. Chapter 6: Using Synapse Spark Pools 10. Chapter 7: Using Databricks Spark Clusters 11. Chapter 8: Streaming Data into Your MDWH 12. Chapter 9: Integrating Azure Cognitive Services and Machine Learning 13. Chapter 10: Loading the Presentation Layer 14. Section 4: Data Presentation, Dashboarding, and Distribution
15. Chapter 11: Developing and Maintaining the Presentation Layer 16. Chapter 12: Distributing Data 17. Chapter 13: Introducing Industry Data Models 18. Chapter 14: Establishing Data Governance 19. Other Books You May Enjoy

Using Structured Streaming with Spark

If you are more the kind of developer that loves to code and you are a fan of Spark, maybe you want to have a look at Structured Streaming with Spark. This might be an interesting alternative for you.

Spark clusters are a widely used engine to implement streaming analytics using one of the available programming languages, such as Python or Scala. With the massive scalability of Spark clusters in Azure services such as Synapse or Databricks, you will be able to implement an environment that can grow with your needs and deliver the necessary performance.

Next to performance, there is the extensibility of Spark clusters that is a factor to consider. You will be able to combine streaming algorithms with the capabilities of Spark and programming languages such as Python (PySpark), Scala, or R.

Take Kafka as input for your streaming analysis, for example. Kafka is an event streaming platform that is quite widely used. ASA does not yet offer...

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