ApacheSpark & Scala

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Syllabus

Dive into Scala:
  • What is Scala
  • Setup and configuration of Scala
  • Develop and run basic Scala Programs
  • Scala operations
  • Functions and procedures in Scala
  • Different Scala APIs for common operations
  • Loops and collections Array, Map, Lists, Tuples
  • Pattern matching for advanced operations
  • Eclipse with Scala
Object Oriented and Functional Programming :
  • Introduction to object oriented programming
  • Different oops concepts
  • Constructor, getter, setter, singleton, overloading and overriding
  • Nested Classes, Visibility Rules
  • Functional Structures
  • Functional programming constructs
  • Call by Name, Call by Value
Big Data and need for Spark :
  • Introduction to Big Data
  • Challenges with old Big Data solutions
  • Batch vs Real-time vs in-Memory processing MapReduce and its limitations
  • Apache Storm and its limitations
  • Need for general purpose solution – Apache Spark
SharePoint Development :
  • Web Parts Overview
  • Default Web Parts inApacheSpark & Scala 2010/2013
  • Web Part Life Cycle
  • Web Parts Development using Web Part Template
  • Web Parts Development using Visual Web Part Template
Deep Dive in Apache Spark:
  • What is Apache Spark?
  • Internals of Spark architecture
  • Apache Spark design principles
  • Spark features and characteristics
  • Apache Spark Eco-system components and their insights
Deploy Spark in Local mode:
  • Setup of Spark Environment
  • Install and configure prerequisites
  • Installation of Apache Spark in local mode
  • Work with Spark in local mode
  • Troubleshooting the encountered problems
Deploy Apache Spark in different modes:
  • Installation of Spark in standalone mode
  • Installation of Spark in YARN mode
  • Installation & configuration of Spark on a real multi-node cluster
  • Play with Spark in cluster mode
  • Best practices for Spark deployment
Demystify Apache Spark:
  • Play with Spark shell
  • Execute Scala and Java statements in shell
  • Understand Spark Context and driver
  • Read data from local filesystem
  • Integrate Spark with HDFS
  • Cache the data in memory for further use
  • Distributed persistence
  • Testing and troubleshooting
Basic Abstraction RDDs :
  • What is RDD in Spark
  • How RDDs make Spark a feature rich framework
  • Transformations in Apache Spark RDDs
  • Spark RDDs action and persistence
  • Spark Lazy operations - Transformation as well as Caching
  • Fault tolerance in Spark
  • Load data and create RDD in Spark
  • Persist RDD in memory or disk
  • Pair operations and key-value in Spark
  • Spark Integration with Hadoop
  • Apache Spark practicals and workshops
Spark streaming :
  • Need for stream analytics
  • Comparison with Storm and S4
  • Real-time data processing using Spark streaming
  • Fault tolerance and check-pointing
  • Stateful Stream Processing
  • DStream and window operations
  • Spark Stream execution flow
  • Connection to various source systems
  • Performance optimizations in Spark
Spark-SQL :
  • What is Spark SQL
  • Apache Spark SQL Features and Data flow
  • Spark SQL architecture and components
  • Hive and Spark SQL together
  • Play with Data frames and Data states
  • Data loading techniques in Spark
  • Hive Queries through Spark
  • Various Spark SQL DDL and DML operations
  • Performance tuning in SparK
Spark MLlib and Spark GraphX :
  • Need for Machine Learning
  • Introduction to Spark machine learning
  • Various Spark ML libraries
  • Algorithms for clustering, statistical analytics, classification etc.
  • Introduction to GraphX
  • Need for different graph processing engine
  • Graph handling using Apache Spark

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