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You're reading from  Distributed Data Systems with Azure Databricks

Product typeBook
Published inMay 2021
Reading LevelBeginner
PublisherPackt
ISBN-139781838647216
Edition1st Edition
Languages
Python
Tools
Azure Functions
Concepts
Data Science
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Author (1)
Alan Bernardo Palacio
Alan Bernardo Palacio
author image
Alan Bernardo Palacio

Alan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a Mechanical Engineering degree from the National University of Tucuman in 2015, participated as the founder in startups, and later on earned a Master's degree from the faculty of Mathematics in the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.
Read more about Alan Bernardo Palacio

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Optimizing queries using DFP

DFP is a Delta Lake feature that automatically skips files that are not relevant to a query. It is a default option in Azure Databricks and works by collecting data about files in Delta Lake, without the need to explicitly state that a file should be skipped on a query, improving performance by making use of the granularity of the data.

The behavior of DFP concerning whether a process is enabled or not, the minimum size of a table, and the minimum number of files needed to trigger a process can be managed by the following options:

  • spark.databricks.optimizer.dynamicPartitionPruning (default is true): Whether DFP is enabled or not.
  • spark.databricks.optimizer.deltaTableSizeThreshold (default is 10 GB): The minimum size of the Delta table that activates DFP.
  • spark.databricks.optimizer.deltaTableFilesThreshold (default is 1000): Represents the number of files of the Delta table on the probe side of the join required to trigger DFP. If the...
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Distributed Data Systems with Azure Databricks
Published in: May 2021Publisher: PacktISBN-13: 9781838647216
© 2021 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Alan Bernardo Palacio

Alan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a Mechanical Engineering degree from the National University of Tucuman in 2015, participated as the founder in startups, and later on earned a Master's degree from the faculty of Mathematics in the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.
Read more about Alan Bernardo Palacio

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