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Cloud Scale Analytics with Azure Data Services

You're reading from  Cloud Scale Analytics with Azure Data Services

Product type Book
Published in Jul 2021
Publisher Packt
ISBN-13 9781800562936
Pages 520 pages
Edition 1st Edition
Languages
Author (1):
Patrik Borosch Patrik Borosch
Profile icon Patrik Borosch

Table of Contents (20) Chapters

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

Loading data

With all the parallel options that the database can offer to you, you want to use them when you load data to your database, too. Remember the purpose of the control and the compute nodes? When loading data to your database, you want to use a technique that makes use of the compute nodes as much as possible.

Using the COPY statement

The COPY statement will support you in doing so. It will talk directly to the compute nodes and will therefore use the whole parallelism that the database can offer. It comes as part of the T-SQL dialect of the Synapse Analytics database and offers many options to influence the loading of data to the database.

When you talk to the control node, in contrast to the capability of the COPY statement, you will create a bottleneck during your load. The load would be single-threaded instead and all the rows that need to be written to the database would first flow through the control node and would then be spread to the distributions using...

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