Apache Spark™ - Lightning-Fast Cluster Computing

Now Make your SuperComputing Cluster 100x Faster and Real Time Analysis with Spark
Cloudera Developer Training

Apache Spark is the next-generation successor to MapReduce. Apache Spark enables participants to build complete, unified Big Data applications combining batch, streaming, and interactive analytics on all their data. With Spark, developers can write sophisticated parallel applications to execute faster decisions, better decisions, and real-time actions, applied to a wide variety of use cases, architectures, and industries.

Cloudera Developer Training for Apache Spark

Apache Spark : BigData Real Time Analysis

FB page:- LinuxWorld India

Apache Spark is the next-generation successor to MapReduce. Spark is a powerful, opensource processing engine for data in the Hadoop cluster, optimized for speed, ease of use,and sophisticated analytics. The Spark framework supports streaming data processing and complex, iterative algorithms, enabling applications to run up to 100x faster than traditional Hadoop MapReduce programs.


  • Why Spark?


  • - Problems with Traditional Large-Scale Systems

    - Introducing Spark

  • Spark Basics


  • -What is Apache Spark? .

    -Using the Spark Shell .

    -Resilient Distributed Datasets (RDDs)

    -Functional Programming with Spark

  • Working with RDDs


  • - RDD Operations

    - Key-Value Pair RDDs

    - MapReduce and Pair RDD Operations

  • The Hadoop Distributed File System


  • - Why HDFS?

    - HDFS Architecture

    - Using HDFS

  • Running Spark on a Cluster

  • - Overview

    - A Spark Standalone Cluster

    - The Spark Standalone Web UI

  • Parallel Programming with Spark


  • -RDD Partitions and HDFS Data Locality

    -Working With Partitions

    -Executing Parallel Operations

  • Caching and Persistence


  • - RDD Lineage.

    - Caching Overview

    - Distributed Persistence

  • Writing Spark Applications


  • -Spark Applications vs. Spark Shell

    -Creating the SparkContext

    -Configuring Spark Properties

  • Spark Streaming

  • -Example: Streaming Word Count>

    -Other Streaming Operations

    -Sliding Window Operations

    -Developing Spark Streaming Applications

  • Common Spark Algorithms

  • -Iterative Algorithms

    -Graph Analysis

    -Machine Learning

  • Improving Spark Performance

  • -Shared Variables: Broadcast Variables

    -Shared Variables: Accumulators

    -Common Performance Issues

  • Writing Spark Applications

  • -Spark Applications vs. Spark Shell

    -Creating the SparkContext

    -Configuring Spark Properties

    -Building and Running a Spark Application

    -Logging
  • This course is best suited to developers and engineers who have programming experience
  • Deep knowledge of Apache Spark
  • Knowledge of Java / Scala is strongly recommended and is required to complete the hands-on exercises. Prior knowledge of Apache Hadoop is recommended

DELIVERANCES BY LINUXWORLD :

  • Training Certificate by LinuxWorld - Training & Development Center
  • Project Certificate by LinuxWorld Informatics Pvt. Ltd. (if prepared under any case study)
  • Latest Software for Spark and Scala
  • Resources - Software & Tools
  • Life Time Support

BENEFITS @ LINUXWORLD:

  • 24 x 7 Wi Fi Enabled Lab Facility
  • Life Time Membership Card
  • Expert faculty having 12 + yrs of industrial exposure
  • Practical Implementation by having hands on experience on live demo and project
  • Job Assistance
 

Further Information

If you would like to know more about this course please ping us @ :
call us on 0091 9829105960 / 0091 141 2501609
send an email to training@lwindia.com or training@linuxworldindia.org

 

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