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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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Understanding the data

After speaking with our marketing team, and the operations team that is collecting our data, we now know that the new data coming in will be conversion event data. We already have data on our geographies and marketing campaigns, and descriptive data on our conversion events.

Let’s first look at the data we already have in our systems. The first table we have is a country geography table with the following schema:

Figure 12.1 – Country dimension schema

Figure 12.1 – Country dimension schema

Let’s take a look at some sample data from this table so we know what’s in it:

Figure 12.2 – Sample country dimension data

Figure 12.2 – Sample country dimension data

In Figure 12.3 we have a conversion events table. It has the following schema:

Figure 12.3 – Conversion event dimension schema

Figure 12.3 – Conversion event dimension schema

Let’s take a look at some sample data from this table so we know what’s in it:

Figure 12.4 – Sample conversion event dimension data

Figure 12.4...

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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