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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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Using Databricks Workflows

Databricks Workflows is a fully managed cloud orchestration service available to all Databricks customers. It simplifies the creation of pipeline orchestration for the following types of tasks:

  • Databricks notebooks
  • Python Script/Wheel
  • JAR
  • Spark Submit
  • Databricks SQL – dashboards, queries, alerts, or files
  • Delta Live Table pipelines
  • dbt

We will focus on using a spark submit task to run a Scala JAR. The first thing we have to do is create an assembly or fat jar, which will include all the dependencies of our project in our JAR.

To do this, we will add the following code to our build.sbt file:

assemblyJarName in assembly := "de-with-scala-assembly-1.0.jar"
assemblyMergeStrategy in assembly := {
case PathList("META-INF", _*) => MergeStrategy.discard
case _ => MergeStrategy.first
}

The first line is to specify the name of the .jar file to be created. The next block will provide a...

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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