Apache Spark with Scala Training in Chennai

Syllabus of Apache Spark with Scala Course in Chennai

Module 1: Introduction

• 1. Overview of Hadoop 
• 2. Architecture of  HDFS  & YARN
• 3. Overview of Spark version 2.2.0
• 4. Spark Architecture
• 5. Spark  Components 
• 6. Comparison of  Spark &  Hadoop
• 7. Installation of Spark v 2.2.0 on Linux 64 bit

Module 2: Spark Core

• 1. Exploring the Spark shell 
• 2. Creating Spark Context
• 3. Operations on Resilient Distributed Dataset – RDD
• 4. Transformations & Actions 
• 5. Loading Data and Saving Data

Module 3: Spark SQL & Hive SQL

• 1. Introduction to SQL  Operations
• 2. SQL Context
• 3. Data Frame
• 4. Working with Hive
• 5. Loading Partitioned Tables
• 6. Processing  CSV, Json ,Parquet files

Module 4: Scala Programming

• 1. Introduction to Scala
• 2. Feature of Scala
• 3. Scala vs Java Comparison
• 4. Data types
• 5. Data Structure
• 6. Arrays
• 7. Literals
• 8. Logical Operators
• 9. Mutable & Immutable variables
• 10. Type interface

Module 5: Scala Functions

• 1. Oops  vs Functions
• 2. Anonymous 
• 3. Recursive 
• 4. Call-by-name
• 5. Currying
• 6. Conditional statement

Module 6: Scala Collections

• 1. List
• 2. Map
• 3. Sets
• 4. Options
• 5. Tuples
• 6. Mutable collection
• 7. Immutable collection
• 8. Iterating
• 9. Filtering and counting 
• 10. Group By
• 11. Flat Map
• 12. Word count
• 13. File Access

Module 7: Scala Object Oriented Programming

• 1. Classes ,Objects & Properties
• 2. Inheritance

Module 8: Spark Submit

• 1. Maven  build tool implementation
• 2. Build Libraries
• 3. Create  Jar files 
• 4. Spark-Submit

Module 9: Spark Streaming

• 1. Overview  of Spark Streaming
• 2. Architecture of Spark Streaming 
• 3. File streaming
• 4. Twitter Streaming

Module 10: Kafka Streaming

• 1. Overview  of Kafka Streaming
• 2. Architecture of Kafka Streaming 
• 3. Kafka Installation
• 4. Topic
• 5. Producer
• 6. Consumer
• 7. File streaming
• 8. Twitter Streaming

Module 11: Spark Mlib

• 1. Overview  of Machine Learning Algorithm
• 2. Linear Regression
• 3. Logistic Regression

Module 12: Spark GraphX

• 1. GraphX overview
• 2. Vertices
• 3. Edges
• 4. Triplets
• 5. Page Rank
• 6. Pregel

Module 13: Performance Tuning

• 1. On-Off-heap memory tuning
• 2. Kryo Serialization
• 3. Broadcast Variable
• 4. Accumulator Variable
• 5. DAG Scheduler
• 6. Data Locality
• 7. Check Pointing
• 8. Speculative Execution
• 9. Garbage Collection

Module 14: Project Planning, Monitoring Trouble Shooting

• 1. Master – Driver Node capacity
• 2. Slave –   Worker Node capacity
• 3. Executor capacity
• 4. Executor core capacity
• 5. Project scenario and execution
• 6. Out-of-memory error handling
• 7. Master logs, Worker logs, Driver  logs
• 8. Monitoring Web UI 
• 9. Heap memory dump