Study Guide for the Developer Certification for Apache Spark Training Video

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A Practical Training Course That Teaches Real World Skills In this project-based Study Guide for the Developer Certification for Apache Spark video tutorial series, you'll quickly have relevant skills for real-world applications. Follow along with our expert instructor in this training course to get: Concise, informative and broadcast-quality Study Guide for the Developer Certification for Apache Spark training videos delivered to your desktop The ability to learn at your own pace with our intuitive, easy-to-use interface A quick grasp of even the most complex Study Guide for the Developer Certification for Apache Spark subjects because they're broken into simple, easy to follow tutorial videos Practical working files further enhance the learning process and provide a degree of retention that is unmatched by any other form of Study Guide for the Developer Certification for Apache Spark tutorial, online or offline... so you'll know the exact steps for your own projects. Course Fast Facts: Only 8 hours to complete this course 68 tutorial videos Expert instructors lead each course Download to any Windows PC or Mac and save for viewing off line Course is accessible 24/7 from any computer once downloaded You can study from home or at work at your own pace in your own time Course Description In this Study Guide for the Developer Certification for Apache Spark training course, expert author Olivier Girardot will teach you everything you need to know to prepare for and pass the Developer Certification for Apache Spark. This course is designed for users that are already familiar with Python, Java, and Scala. 

You will start by learning about Apache Spark best practices, including transformations, actions, and joins. From there, Olivier will teach you about closure serialization, shared variables and performance, and Spark SQL. This video tutorial also covers Spark MLLib, Spark...

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Windows
Apache
SQL
Performance
Project
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Programa académico

  • 01. Introduction
    • Introduction And Course Overview
    • About The Author
    • Spark Concepts And Approach
    • Resilient Distributed Databases - RDD
    • Creating A Project In IDEA
    • 0106 How To Access Your Working Files
  • 02. Spark Core API And Best practices
    • 0201 Base RDD
    • 0202 Transformations
    • 0203 Actions - Part 1
    • 0204 Actions - Part 2
    • 0205 Hadoop Combiners In Spark
    • 0206 Direct Acyclic Graph And Lazy Evaluation
    • 0207 Joins
  • 03. Closure Serialization
    • 0301 How Does The Magic Of Spark Work?
    • 0302 Serializers And How To Change Them
  • 04. Shared Variables And Performance
    • 0401 Broadcast
    • 0402 Accumulators
    • 0403 Caching And Persistence
  • 05. Spark SQL
    • 0501 Spark SQL
    • 0502 Inferring A Schema
    • 0503 Applying A Schema
    • 0504 Loading And Writing
    • 0505 SQL Caching And UDF
  • 06. Spark MLLib
    • 0601 Spark MLLib And Supervised Example - SVM
    • 0602 Unsupervised With Iris Dataset - KMeans
  • 07. Spark GraphX
    • 0701 Graph Construction
    • 0702 Graph Algorithms
  • 08. Spark Streaming
    • 0801 Streaming And The Microbatch
    • 0802 Mutable Transformations And Checkpointing
    • 0803 Windows And RDD Transformations
    • 0804 Streaming With Spark SQL, MLLib And Core
  • 09. Deployment And Infrastructure
    • 0901 Cluster Managers And Submission - Standalone, Mesos And Yarn
  • 10. Conclusion
    • 1001 Resources And Where To Go From Here

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