Apache Spark Training Institute in Bangalore| Apache Spark | Blue Ocean Learning
Apache Spark classes in jp nagar 7th phase Banglore

Apache Spark

Module 1-Why Spark? Explain Spark and Hadoop Distributed File System

  • What is Spark
  • Comparison with Hadoop
  • Components of Spark

Module 2-Spark Components, Common Spark Algorithms-Iterative Algorithms, Graph Analysis, Machine Learning

  • Apache Spark- Introduction, Consistency, Availability, Partition
  • Unified Stack Spark
  • Spark Components
  • Comparison with Hadoop – Scalding example, mahout, storm, graph
  • Module 3-Running Spark on a Cluster, Writing Spark Applications using Python, Java, Scala

    • Explain python example
    • Show installing a spark
    • Explain driver program
    • Explaining spark context with example
    • Define weakly typed variable
    • Combine scala and java seamlessly.
    • Explain concurrency and distribution.
    • Explain what is trait.
    • Explain higher order function with example.
    • Define OFI scheduler.
    • Advantages of Spark
    • Example of Lamda using spark
    • Explain Mapreduce with example

    Module 4-RDD and its operation

    • Difference between RISC and CISC
    • Define Apache Mesos
    • Cartesian product between two RDD
    • Define count
    • Define Filter
    • Define Fold
    • Define API Operations
    • Define Factors

    Module 5-Spark, Hadoop, and the Enterprise Data Centre, Common Spark Algorithms

    • How hadoop cluster is different from spark
    • Define writing data
    • Explain sequence file and its usefulness
    • Define protocol buffers
    • Define text file, CSV, Object Files and File System
    • Define sparse metrics
    • Explain RDD and Compression
    • Explain data stores and its usefulness

    Module 6-Spark Streaming

    • Define Elastic Search
    • Explain Streaming and its usefulness
    • Apache bookeeper
    • Define Dstream
    • Define mapreduce word count
    • Explain Paraquet
    • Scala ORM
    • Define Mlib
    • Explain multi graphix and its usefulness
    • Define property graph

    Mini Projects

    • Project 1. List the items
    • Project 2. Sorting of Records
    • Project 3. Show a histogram of date vs users created. Optionally, use a rich visualization like
    • Project 4. Prepare a map of tags vs # of questions in each tag and display it.

    Major Projects

    • Project 1 Movie Recommendation
    • Project 2 Twitter API Integration for tweet Analysis
    • Project 3 Data Exploration Using Spark SQL – Wikipedia dataset