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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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Local environment setup

In this chapter, we are going to look at some of the tools that the rest of the book will use.

The build tool

We will be using sbt as the build tool. It was the first build tool that was specifically created for Scala. sbt uses a standard project directory structure underneath the main project directory, also called the base directory in sbt parlance. For example, if we created a project named hello in the /tmp/foo directory, then /tmp/foo is the base directory of the project. Here is what a typical directory structure will look like under the base directory:

build.sbt
project/
src/
-- main/
   |-- java/
   |-- resources/
   |-- scala/
|-- test/
   |-- java/
   |-- resources/
   |-- scala/
target/

The build definition is described in the build.sbt file in the project’s base directory. In addition to build.sbt, the project directory can contain .scala...

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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