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You're reading from  Azure Data Engineer Associate Certification Guide

Product typeBook
Published inFeb 2022
PublisherPackt
ISBN-139781801816069
Edition1st Edition
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Newton Alex
Newton Alex
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Newton Alex

Newton Alex leads several Azure Data Analytics teams in Microsoft, India. His team contributes to technologies including Azure Synapse, Azure Databricks, Azure HDInsight, and many open source technologies, including Apache YARN, Apache Spark, and Apache Hive. He started using Hadoop while at Yahoo, USA, where he helped build the first batch processing pipelines for Yahoo's ad serving team. After Yahoo, he became the leader of the big data team at Pivotal Inc., USA, where he was responsible for the entire open source stack of Pivotal Inc. He later moved to Microsoft and started the Azure Data team in India. He has worked with several Fortune 500 companies to help build their data systems on Azure.
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Configuring the batch size

To configure the batch size, we will explore how to determine the batch size in Azure Batch. Batch size refers to both the size of Batch pools and the size of the VMs in those pools. The following guidelines are generic enough that they can be applied to other services such as Spark and Hive too.

Here are some of the points to consider while deciding on the batch size:

  • Application requirements: Based on whether the application is CPU-intensive, memory-intensive, storage-intensive, or network-intensive, you will have to choose the right types of VMs and the right sizes. You can find all the supported VM sizes using the following Azure CLI command (here, centralus is an example):
    az batch location list-skus –location centralus
  • Data profile: If you know how your input data is spread, it will help in deciding the VM sizes that will be required. We will have to plan for the highest amount of data that will be processed by each of the VMs.
  • ...
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Azure Data Engineer Associate Certification Guide
Published in: Feb 2022Publisher: PacktISBN-13: 9781801816069

Author (1)

author image
Newton Alex

Newton Alex leads several Azure Data Analytics teams in Microsoft, India. His team contributes to technologies including Azure Synapse, Azure Databricks, Azure HDInsight, and many open source technologies, including Apache YARN, Apache Spark, and Apache Hive. He started using Hadoop while at Yahoo, USA, where he helped build the first batch processing pipelines for Yahoo's ad serving team. After Yahoo, he became the leader of the big data team at Pivotal Inc., USA, where he was responsible for the entire open source stack of Pivotal Inc. He later moved to Microsoft and started the Azure Data team in India. He has worked with several Fortune 500 companies to help build their data systems on Azure.
Read more about Newton Alex