Introduction to PySpark Training Video

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AU$ 189 - (Rs 9,535)
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  • Course
  • Online
  • When:
    Flexible
Description

A Practical Training Course That Teaches Real World Skills In this project-based Introduction to PySpark 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 Introduction to PySpark 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 Introduction to PySpark 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 Introduction to PySpark tutorial, online or offline... so you'll know the exact steps for your own projects. Course Fast Facts: Only 3.5 hours to complete this course 60 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 Introduction to PySpark training course, expert author Alex Robbins will teach you everything you need to know about the Spark Python API. This course is designed for users that already have a basic working knowledge of Python.

You will start by learning how to install Spark, then jump into learning the Spark fundamentals. From there, Alex will teach you about transformations, including filter, pipe, repartition, and distinct. This video tutorial also covers actions, input and output, performance, and running on a cluster. Finally, you will learn advanced topics, including Spark streaming, dataframes and SQL, and MLlib.

Once you have completed this computer based training course, you will have learned everything you need to know about PySpark. Working...

Important information

Requirements: System Requirements - Digital Download Digital Download: Microsoft Windows XP or higher, Mac OS X 10.4 or higher. Minimum screen resolution of 1024x768 Digital Download specific requirements: Between 1GB and 6GB of available hard drive space (depending on the training course) An Internet connection with sufficient bandwidth. You must have at least a 56K modem connection (Broadband recommended). Most modern ADSL and Cable internet solutions will be sufficient. Do I need...

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Where and when

Starts Location
Flexible
Online

What you'll learn on the course

SQL
Performance
Broadcast
Skills and Training

Course programme

  • 01. Introduction
    • Introduction And Course Overview
    • About The Author
    • Installing Python
    • Installing iPython And Using Notebooks
    • 0105 How To Access Your Working Files
  • 02. Installing Spark
    • Download And Setup
    • Running The Spark Shell
    • Running The Spark Shell With iPython
  • 03. Spark Fundamentals
    • 0301 What Is A Resilient Distributed Dataset - RDD
    • 0302 Reading A Text File
    • 0303 Actions
    • 0304 Transformations
    • 0305 Persisting Data
  • 04. Transformations
    • 0401 Map
    • 0402 Filter
    • 0403 Flatmap
    • 0404 MapPartitions
    • 0405 MapPartitionsWithIndex
    • 0406 Sample
    • 0407 Union
    • 0408 Intersection
    • 0409 Distinct
    • 0410 Cartesian
    • 0411 Pipe
    • 0412 Coalesce
    • 0413 Repartition
    • 0414 RepartitionAndSortWithinPartitions
  • 05. Actions
    • 0501 Reduce
    • 0502 Collect
    • 0503 Count
    • 0504 First
    • 0505 Take
    • 0506 TakeSample
    • 0507 TakeOrdered
    • 0508 SaveAsTextFile
    • 0509 CountByKey
    • 0510 ForEach
  • 06. Key-Value Pair RDDs
    • 0601 GroupByKey
    • 0602 ReduceByKey
    • 0603 AggregateByKey
    • 0604 SortByKey
    • 0605 Join
    • 0606 CoGroup
  • 07. Input And Output
    • 0701 WholeTextFile
    • 0702 Pickle Files
    • 0703 HadoopInputFormat
    • 0704 HadoopOutputFormat
  • 08. Performance
    • 0801 Broadcast Variables
    • 0802 Accumulators
    • 0803 Using A Custom Accumulator
    • 0804 Partitioning
  • 09. Running On A Cluster
    • 0901 Spark Standalone Cluster
    • 0902 Mesos
    • 0903 Yarn
    • 0904 Client Versus Cluster Mode
  • 10. Advanced Spark
    • 1001 Spark Streaming
    • 1002 Dataframes And SQL
    • 1003 MLlib
  • 11. Conclusion
    • 1101 Resources And Where To Go From Here
    • 1102 Wrap Up

Additional information

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