Bigdata Online Expert level by Indian Institute of Hardware Technology

Java Fundamentals
Basic java concepts
Multi-threading
File I/O –Java. IO
Collections –Java.Util.*, Java.Math, Java.Lang
Java Generics
Java Serialization
Java Database Connectivity –JDBC
Java Common Design Patterns
Java Open Source Frameworks (Spring, Apache Maven, Logging, etc...)
Java Apache Hadoop Frameworks (Hadoop Common, Map Reduce etc.)
Understand Web Servers & Application Servers - JBoss Application server, Apache Tomcat server
Java Unit testing Frameworks (Junit / TestNG)
Eclipse IDE – Java Development.
Version Control – GIT, SVN, etc.
Java Continuous Integration frameworks – Husdson, Jenkins, etc.
Handling XML and XSD using Java frameworks
Java XML Parsers frameworks – DOM and SAX
Java Web services concepts – SOA, SOAP, XML, JAXB,
SOAP Web services
REST web services

Hadoop Fundamentals
What is Big Data? Why Big Data?
Hadoop Architecture & Components
Hadoop Storage & File Formats (ASCII, Avro, Parquet, RC4, JSON, EBCDIC etc.)
Hadoop Processing – Map Reduce, Spark Frameworks

HDFS
HDFS Basics
File Storage
Fault Tolerance

Map Reduce
What Is MapReduce?
Basic MapReduce Concepts
Concepts of Mappers, Reducers, Combiners and Paritioning
Inputs and Output formats to MR Program
Error Handling and creating UDFs for MR

Spark
What Is Spark?
Basic Spark Concepts
How Spark differs from Map Reduce?
Working with RDD’s
Parallel Programming with Spark
Spark Streaming

Hive
What is Hive, why we need it and its importance in DWH?
How Hive is different from Traditional RDBMS
Modeling in Hive, creating Hive structures and data load process.
Concepts of Partitioning, Bucketing, Blocks, Hashing, External Tables etc.
Concepts of serialization, deserialization
Different Hive data storage formats including ORC, RC, and Parquet.
Introduction ton HiveQL and examples.
Hive as an ELT tool and difference between Pig and Hive
Performance tuning opportunities in Hive, learnings and Best Practices.
Writing and mastering Hive UDFs
Error Handling and scope of creating Hive UDFs.

Pig and Latin
Basics of Pig and Why Pig?
Grunt
Pig’s Data Model
Writing Evaluation
Filter
Load & Store Functions
Benefits of Pig over SQL language
Input and Output formats to MR program.
Error Handling and scope of creating UDFs for Pig.

HBase
HBase – Introduction
When to use HBase
HBase Data Model
HBase Families & Components
Data Storage and Distribution
HBase Master

Sqoop
Sqoop Overview
Sqoop Exercises

Yarn
YARN Overview
HDFS 2

MongoDB
Introduction to In-Memory Computing
When to use MongoDB
MongoDB API
Indexing and Data Modeling
Drivers / Replication / Sharding

Hadoop Security
Security Overview
Knox Exercise
Access Control Labels