Spark Online Training

About Spark Online Training
Course Objectives
- Â 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.
- Â Any IT experienced Professional who are interested to build their career in Big data analysts using the spark.
- Â Any B.E/ B.Tech/ BSC/ M.C.A/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
- Fresh Graduates.
- Â 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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