Spark Online Training

Spark Online Training

Apache-Spark-online-training-nareshit

About Spark Online Training

Spark is a cluster computer framework. It is an open source and lighting fast computer designer. It is used or extends the MapReduce model. It was introduced by Apache framework. It enables a computing solution which is scalable.  Also, it is flexible and cost-effective.  It is used for the speed up the Hadoop computational computing software process. Spark has its own cluster management, hence it uses the Hadoop for the storage purpose.

Course Objectives

Apache Spark Course Objectives
After completion of the Apache spark Course at Naresh I technologies, you will be expertise and eligible for:
  •  Complete knowledge of Scala and Apache Spark.
  • Learn algorithm of Spark.
  • Learn Functional and Object-Oriented Programming Paradigms.
  • Learn Spark Ecosystem Component of Hadoop.
  • Work on Spark Streaming.
  • Knowledge of Graphics spark SQL.
Who should go for this course
  •  Any IT experienced Professional who are interested to build their career in Big data analysts using the spark.
  •  Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
  • Fresh Graduates.
Pre-requisites
The course can learn from any IT professional having basic knowledge of :
  •  Core Java
  • Database  concept
  • Unix/Linux

Course Curriculum

  • Introduction to Apache Spark
  • Why Spark
  • Batch Vs. Real Time Big Data Analytics
  • Batch Analytics – Hadoop Ecosystem Overview,
  • Real Time Analytics Options,
  • Streaming Data – Storm,
  • In Memory Data – Spark, What is Spark?,
  • Spark benefits to Professionals
  • Limitations of MR in Hadoop
  • Components of Spark
  • Spark Execution Architecture
  • Benefits of Apache Spark
  • Hadoop vs Spark
  • Features of Scala
  • Basic Data Types of Scala
  • Val vs Var
  • Type Inference
  • REPL
  • Objects & Classes in Scala
  • Functions as Objects in Scala
  • Anonymous Functions in Scala
  • Higher Order Functions
  • Lists in Scala
  • Maps
  • Pattern Matching
  • Traits in Scala
  • Collections in Scala
  • Spark & Distributed Systems
  • Spark for Scalable Systems
  • Spark Execution Context
  • What is RDD
  • RDD Deep Dive
  • RDD Dependencies
  • RDD Lineage
  • Spark Application In Depth
  • Spark Deployment
  • Parallelism in Spark
  • Caching in Spark
  • Spark Transformations
  • Spark Actions
  • Spark Cluster
  • Spark SQL Introduction
  • Spark Data Frames
  • Spark SQL with CSV
  • Spark SQL with JSON
  • Spark SQL with Database
  • Features of Spark Streaming
  • Micro Batch
  • Dstreams
  • Transformations on Dstreams
  • Spark Streaming Use Case 1
  • Spark Streaming Use Case 2
  • Spark Streaming Use Case 3
  • Introduction to Graph Parallel Systems
  • Introduction to GraphX
  • Features of GraphX
  • GraphX Deep Dive
  • Graph Builder
  • Using Mllib for Movie Recommendations
  • Analyzing Recommendation Results using Spark


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