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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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Defining constraints

In the previous section, we looked at examples of how Deequ can automatically suggest constraints as well as how we can gather various metrics around data. We will now define the actual constraints that we expect the dataframe to pass. In the following code, we define the following constraints that we expect the flights data to pass:

  • The airline column should not contain any NULL values
  • The flight_number column should not contain any NULL values
  • The cancelled column should contain only 0 or 1
  • The distance column should not contain any negative value
  • The cancellation_reason column should contain only A, B, C, or D

If all of the checks pass, then we print data looks good on the console; else, we print the constraint along with the result status.

Here is the code for it.

As a first step, we will create a dataframe using the flights table we loaded in MySQL:

  val session = Spark.initSparkSession("de-with-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