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Data Engineering with Scala and Spark: Build streaming and batch pipelines that process massive amounts of data using Scala (en Inglés)
Eric Tome
(Autor)
·
Rupam Bhattacharjee
(Autor)
·
David Radford
(Autor)
·
Packt Publishing
· Tapa Blanda
Data Engineering with Scala and Spark: Build streaming and batch pipelines that process massive amounts of data using Scala (en Inglés) - Tome, Eric ; Bhattacharjee, Rupam ; Radford, David
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Origen: Estados Unidos
(Costos de importación incluídos en el precio)
Se enviará desde nuestra bodega entre el
Martes 04 de Junio y el
Viernes 21 de Junio.
Lo recibirás en cualquier lugar de España entre 1 y 5 días hábiles luego del envío.
Reseña del libro "Data Engineering with Scala and Spark: Build streaming and batch pipelines that process massive amounts of data using Scala (en Inglés)"
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 dataKey FeaturesTransform data into a clean and trusted source of information for your organization using ScalaBuild streaming and batch-processing pipelines with step-by-step explanationsImplement 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 eBookBook DescriptionMost 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 learnSet up your development environment to build pipelines in ScalaGet to grips with polymorphic functions, type parameterization, and Scala implicitsUse Spark DataFrames, Datasets, and Spark SQL with ScalaRead and write data to object storesProfile and clean your data using DeequPerformance tune your data pipelines using ScalaWho this book is forThis 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 ContentsScala Essentials for Data EngineersEnvironment SetupAn Introduction to Apache Spark and Its APIs - DataFrame, Dataset, and Spark SQLWorking with DatabasesObject Stores and Data LakesUnderstanding Data TransformationData Profiling and Data QualityTest-Driven Development, Code Health, and MaintainabilityCI/CD with GitHubData Pipeline OrchestrationPerformance TuningBuilding Batch Pipelines Using Spark and ScalaBuilding Streaming Pipelines Using Spark and Scala
