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Data enginerring with Scala and Spark : a practical guide helping you build streaming and batch... pipelines that process massive amounts of data usi /
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Tome, Eric, author. Bhattachiarjee, Rupam, author. Radford, David E., author. |
| 서명/저자사항 | Data enginerring with Scala and Spark :a practical guide helping you build streaming and batch... pipelines that process massive amounts of data usi /Eric Tome, Rupam Bhattachiarjee, David Radford. |
| 발행사항 | Birmingham, UK : Packt Publishing, 2024. |
| 형태사항 | 1 online resource (300 pages) : illustrations |
| 소장본 주기 | Added to collection customer.56279.3 |
| ISBN | 9781804614327 1804614327 |
| 일반주기 |
Includes index.
|
| 요약 | 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. |
| 일반주제명 | Software engineering. Programming languages (Electronic computers) |
| 언어 | 영어 |
| 기타형태 저록 | Print version:18046125889781804612583 |
| 대출바로가기 | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3779293 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00030544 | 005.1 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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