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Data Engineering with Scala and Spark

You're reading from  Data Engineering with Scala and Spark

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781804612583
Pages 300 pages
Edition 1st Edition
Languages
Concepts
Authors (3):
Eric Tome Eric Tome
Profile icon Eric Tome
LinkedIn
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.
See other products by Eric Tome
Rupam Bhattacharjee Rupam Bhattacharjee
Profile icon Rupam Bhattacharjee
LinkedIn
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.
See other products by Rupam Bhattacharjee
David Radford David Radford
Profile icon David Radford
LinkedIn
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.
See other products by David Radford
View More author details

Table of Contents (21) Chapters

Preface Part 1 – Introduction to Data Engineering, Scala, and an Environment Setup
Chapter 1: Scala Essentials for Data Engineers Chapter 2: Environment Setup Part 2 – Data Ingestion, Transformation, Cleansing, and Profiling Using Scala and Spark
Chapter 3: An Introduction to Apache Spark and Its APIs – DataFrame, Dataset, and Spark SQL Chapter 4: Working with Databases Chapter 5: Object Stores and Data Lakes Chapter 6: Understanding Data Transformation Chapter 7: Data Profiling and Data Quality Part 3 – Software Engineering Best Practices for Data Engineering in Scala
Chapter 8: Test-Driven Development, Code Health, and Maintainability Chapter 9: CI/CD with GitHub Part 4 – Productionalizing Data Engineering Pipelines – Orchestration and Tuning
Chapter 10: Data Pipeline Orchestration Chapter 11: Performance Tuning Part 5 – End-to-End Data Pipelines
Chapter 12: Building Batch Pipelines Using Spark and Scala Chapter 13: Building Streaming Pipelines Using Spark and Scala Index Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

action 126

examples of action functions 126

Adaptive Query Execution (AQE) 229

advanced window functions

leveraging 140-142

Amazon Elastic Compute Cloud (EC2) 108

Amazon Web Services (AWS) 106, 202

anomalies

detecting 159-161

Apache Airflow

core features 200

data pipelines, designing 205

deployment options 202

directed acyclic graphs (DAGs) 200

extensibility 201

hosting options 202

monitoring 202

operators 200, 201

scheduling 200

task dependencies 200

user interface (UI) 202

Apache Spark

download link 43

reference link 49

working with 64

application programming interface (API) 75

Argo

used, for orchestrating batch pipeline 251-256

Argo workflows

core components 207

creating 209-213

installing 206

...

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Data Engineering with Scala and Spark
Published in: Jan 2024 Publisher: Packt ISBN-13: 9781804612583
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Authors (3)

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Profile icon Eric Tome
LinkedIn
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.
See other products by Eric Tome
Profile icon Rupam Bhattacharjee
LinkedIn
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.
See other products by Rupam Bhattacharjee
Profile icon David Radford
LinkedIn
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.
See other products by David Radford
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