LIN | Cloud Hadoop: Scaling Apache Spark (2020 EN)

Discussion in 'Information Technology' started by Kanka, Apr 4, 2020.

  1. Kanka

    Kanka Well-Known Member Loyal User

    Messages:
    13,562
    Likes Received:
    364
    Trophy Points:
    83
    [​IMG]

    Company: Linkedin Learning
    Author: Lynn Langit
    Full Title: Cloud Hadoop: Scaling Apache Spark
    Year: 2020
    Language: English
    Genre: Educational: Big Data
    Skill Level: Beginner
    Price: €24.99
    -
    Files: MP4 (+ Exercise Files, Subtitles .SRT)
    Time: 03:13:26
    Video: AVC, 1280 x 720 (1.778) at 15.000 fps, 200 kbps
    Audio: AAC at 160 Kbps, 2 channels, 48.0 KHz



    Apache Hadoop and Spark make it possible to generate genuine business insights from big data. The Amazon cloud is natural home for this powerful toolset, providing a variety of services for running large-scale data-processing workflows. Learn to implement your own Apache Hadoop and Spark workflows on AWS in this course with big data architect Lynn Langit. Explore deployment options for production-scaled jobs using virtual machines with EC2, managed Spark clusters with EMR, or containers with EKS. Learn how to configure and manage Hadoop clusters and Spark jobs with Databricks, and use Python or the programming language of your choice to import data and execute jobs. Plus, learn how to use Spark libraries for machine learning, genomics, and streaming. Each lesson helps you understand which deployment option is best for your workload.


    Topics include:
    01. File systems for Hadoop and Spark
    02. Working with Databricks
    03. Loading data into tables
    04. Setting up Hadoop and Spark clusters on the cloud
    05. Running Spark jobs
    06. Importing and exporting Python notebooks
    07. Executing Spark jobs in Databricks using Python and Scala
    08. Importing data into Spark clusters
    09. Coding and executing Spark transformations and actions
    10. Data caching
    11. Spark libraries: Spark SQL, SparkR, Spark ML, and more
    12. Spark streaming
    13. Scaling Spark with AWS and GCP


    -------------