rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Simplify Big Data Analytics with Amazon EMR
    sub title: a Beginner's Guide to Learning and Implementing Amazon EMR for Building Data Analytics Solutions
  name:
    name part: Mishra, Sakti
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  origin info:
    place:
      place term: enk
    place:
      place term: Birmingham
    publisher: Packt Publishing, Limited
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (430 pages)
  abstract: Design scalable big data solutions using Hadoop, Spark, and AWS cloud native services Key Features Build data pipelines that require distributed processing capabilities on a large volume of data Discover the security features of EMR such as data protection and granular permission management Explore best practices and optimization techniques for building data analytics solutions in Amazon EMR Book Description Amazon EMR, formerly Amazon Elastic MapReduce, provides a managed Hadoop cluster in Amazon Web Services (AWS) that you can use to implement batch or streaming data pipelines. By gaining expertise in Amazon EMR, you can design and implement data analytics pipelines with persistent or transient EMR clusters in AWS. This book is a practical guide to Amazon EMR for building data pipelines. You'll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports. You'll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi. Finally, you'll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you'll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR. By the end of this book, you'll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS. What you will learn Explore Amazon EMR features, architecture, Hadoop interfaces, and EMR Studio Configure, deploy, and orchestrate Hadoop or Spark jobs in production Implement the security, data governa…
  subject:
    title info:
      title: MapReduce (Computer file)
  subject:
    title info:
      title: MapReduce (Computer file)
  subject:
    topic: Big data
  subject:
    topic: Données volumineuses
  subject:
    topic: COMPUTERS
    topic: Data Processing
  subject:
    topic: COMPUTERS
    topic: Intelligence (AI) & Semantics
  subject:
    topic: COMPUTERS
    topic: Data Visualization
  subject:
    topic: Big data
  classification: QA76.9.D32
  classification: 005.7
  related item:
    title info:
      title: Simplify Big Data Analytics with Amazon EMR
    name:
      name part: Mishra, Sakti.
    origin info:
      publisher: Birmingham : Packt Publishing, Limited, ©2022
  identifier: 180107772X
  identifier: 9781801077729
  identifier: 9781801071079 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: EBLCP
    record creation date: 220319
    record change date: 20260128150958.9
    record identifier: in00004670705
    language of cataloging:
      language term: eng