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Data Engineering with Scala and Spark: A practical guide helping you build streaming and batch pipelines that process massive amounts of data using Scala

Data Engineering with Scala and Spark: A practical guide helping you build streaming and batch pipelines that process massive amounts of data using Scala

Data Engineering with Scala and Spark: A practical guide helping you build streaming and batch pipelines that process massive amounts of data using Scala Paperback / softback - 2024

by Eric Tome

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Paperback / softback. New. Take your data engineering skills to the next level by learning to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data Key Features Transform data into a clean and trusted source of information for your organization using Scala Build streaming and batch-processing pipelines with step-by-step explanations Implement and orchestrate your pipelines by following CI/CD best practices and test-driven development Book DescriptionPerformance in distributed computing environments is a critical factor for data engineers. If performance in a distributed computing environment is not optimal, several consequences can arise like slow data processing, bottlenecks and latency, inefficient resource utilization, etc. impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount. This book will teach you to leverage the Scala programming language on the Spark framework and the latest cloud technologies to build continuous and triggered data pipelines. You will do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You will then understand DataFrame API, Dataset API, and Spark SQL API and its use. You will further learn about data profiling and quality in Scala. You will also orchestrate and performance-tune your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.What you will learn Set up your development environment to build pipelines in Scala Use polymorphic functions, type parameterization, and scala implicit Use Spark DataFrames, Datasets, and Spark SQL with Scala Read and write data to object stores Build and chain data transforms using Scala Profile and clean your data using Deequ Performance-tune your data pipelines using Scala Who this book is forThis book is aimed at data engineers who are experienced in working with data but want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies.
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Details

  • Title Data Engineering with Scala and Spark: A practical guide helping you build streaming and batch pipelines that process massive amounts of data using Scala
  • Author Eric Tome
  • Binding Paperback
  • Condition New
  • Pages 300
  • Volumes 1
  • Language ENG
  • Publisher Packt Publishing
  • Publication date 2024-01-31
  • Bookseller's Inventory # B9781804612583
  • ISBN 9781804612583 / 1804612588
  • Weight 1.14 lbs (0.52 kg)
  • Dimensions 9.25 x 7.5 x 0.63 in (23.50 x 19.05 x 1.60 cm)
  • Category Computers - Data Base Management
  • Quantity available 10

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From the publisher

Take your data engineering skills to the next level by learning how to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data

Key Features

- Transform data into a clean and trusted source of information for your organization using Scala

- Build streaming and batch-processing pipelines with step-by-step explanations

- Implement and orchestrate your pipelines by following CI/CD best practices and test-driven development (TDD)

- Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Most data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount.

This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You'll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You'll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users.

By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.

What you will learn

- Set up your development environment to build pipelines in Scala

- Get to grips with polymorphic functions, type parameterization, and Scala implicits

- Use Spark DataFrames, Datasets, and Spark SQL with Scala

- Read and write data to object stores

- Profile and clean your data using Deequ

- Performance tune your data pipelines using Scala

Who this book is for

This book is for data engineers who have experience in working with data and want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies.

Table of Contents

- Scala Essentials for Data Engineers

- Environment Setup

- An Introduction to Apache Spark and Its APIs - DataFrame, Dataset, and Spark SQL

- Working with Databases

- Object Stores and Data Lakes

- Understanding Data Transformation

- Data Profiling and Data Quality

- Test-Driven Development, Code Health, and Maintainability

- CI/CD with GitHub

- Data Pipeline Orchestration

- Performance Tuning

- Building Batch Pipelines Using Spark and Scala

- Building Streaming Pipelines Using Spark and Scala

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