You're reading from Data Engineering with Scala and Spark

Product typeBook

Published inJan 2024

PublisherPackt

ISBN-139781804612583

Edition1st Edition

Concepts

Data Engineering

Authors (3):

Eric Tome

Rupam Bhattacharjee

David Radford

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Part 4 – Productionalizing Data Engineering Pipelines – Orchestration and Tuning

In this part, Chapter 10 delves into data pipeline orchestration, focusing on seamless task coordination and failure handling. It introduces tools such as Apache Airflow, Argo, Databricks Workflows, and Azure Data Factory. Chapter 11 highlights the Spark UI’s significance in performance optimization, covering the basics, tuning, resource optimization, and data handling techniques such as skewing, indexing, and partitioning.

This part has the following chapters:

Chapter 10, Data Pipeline Orchestration
Chapter 11, Performance Tuning

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Data Engineering with Scala and Spark

Published in: Jan 2024Publisher: PacktISBN-13: 9781804612583

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Authors (3)

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 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 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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