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You're reading from  Azure Data Engineer Associate Certification Guide

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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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Understanding the basics of partitioning

In the previous chapter, we briefly introduced the concept of partitioning as part of the Designing storage for efficient querying section. We explored storage-side partitioning concepts such as replicating data, reducing cross-partition operations such as joins, and eventual consistency to improve query performance. In this chapter, we will deep dive more systematically into both storage and analytical partitioning techniques. Let's start with the benefits of partitioning.

Benefits of partitioning

Partitioning has several benefits apart from just query performance. Let's take a look at a few important ones.

Improving performance

As we discussed in the previous chapter, partitioning helps improve the parallelization of queries by splitting massive monolithic data into smaller, easily consumable chunks.

Apart from parallelization, partitioning also improves performance via data pruning, another concept that we already...

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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