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You're reading from  Data Engineering with Scala and Spark

Product typeBook
Published inJan 2024
PublisherPackt
ISBN-139781804612583
Edition1st Edition
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Authors (3):
Eric Tome
Eric Tome
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Eric Tome

Eric Tome has over 25 years of experience working with data. He has contributed to and led teams that ingested, cleansed, standardized, and prepared data used by business intelligence, data science, and operations teams. He has a background in mathematics and currently works as a senior solutions architect at Databricks, helping customers solve their data and AI challenges.
Read more about Eric Tome

Rupam Bhattacharjee
Rupam Bhattacharjee
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Rupam Bhattacharjee

Rupam Bhattacharjee works as a lead data engineer at IBM. He has architected and developed data pipelines, processing massive structured and unstructured data using Spark and Scala for on-premises Hadoop and K8s clusters on the public cloud. He has a degree in electrical engineering.
Read more about Rupam Bhattacharjee

David Radford
David Radford
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David Radford

David Radford has worked in big data for over 10 years, with a focus on cloud technologies. He led consulting teams for several years, completing a migration from legacy systems to modern data stacks. He holds a master's degree in computer science and works as a senior solutions architect at Databricks.
Read more about David Radford

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How do Spark applications work?

A Spark application runs on a Spark cluster, which is a connected group of nodes. These nodes can be virtual machines (VMs) or bare-metal servers. In terms of Spark architecture, there is one driver node and one to n executors that run on your Spark cluster. The driver will control the executors and provide instructions (defined in your Spark application) to the executors. Generally, the driver never actually touches the data you are processing. The executors are where data is manipulated, given instructions from the driver. This is depicted in the following diagram:

Figure 3.1 – Spark driver and executor architecture

Figure 3.1 – Spark driver and executor architecture

Note that the following calculations assume linear scalability, which is not always the case. The actual gain from distributing the work across many nodes depends on the nature of the data and the transformations applied to the data.

On open source Spark, you can configure the number of executors...

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Data Engineering with Scala and Spark
Published in: Jan 2024Publisher: PacktISBN-13: 9781804612583

Authors (3)

author image
Eric Tome

Eric Tome has over 25 years of experience working with data. He has contributed to and led teams that ingested, cleansed, standardized, and prepared data used by business intelligence, data science, and operations teams. He has a background in mathematics and currently works as a senior solutions architect at Databricks, helping customers solve their data and AI challenges.
Read more about Eric Tome

author image
Rupam Bhattacharjee

Rupam Bhattacharjee works as a lead data engineer at IBM. He has architected and developed data pipelines, processing massive structured and unstructured data using Spark and Scala for on-premises Hadoop and K8s clusters on the public cloud. He has a degree in electrical engineering.
Read more about Rupam Bhattacharjee

author image
David Radford

David Radford has worked in big data for over 10 years, with a focus on cloud technologies. He led consulting teams for several years, completing a migration from legacy systems to modern data stacks. He holds a master's degree in computer science and works as a senior solutions architect at Databricks.
Read more about David Radford