Big Data (Hadoop)

Hadoop is one of the important tool (framework) developed to handle Big Data. It is an open-source program where Big Data is systematically distributed on clusters of commodity hardware. Hadoop is primarily being useful where huge data has to be stored. Big data training will provide opportunities for you to achieve great success at the later stage of your career.

We offer an excellent opportunity for your career growth. Once you master your skills on Big Data Hadoop, you can easily start up with Data Analyst in any of the MNCs or software companies.

Hadoop training in Chandigarh through our organization, you can enhance your skills which goes a long way in shaping up your career. For best learning of Big Data Hadoop, enroll at our organization from where you will sharpen your skillset for optimum usage in any of the top MNCs.

Hadoop certification training in Chandigarh only at INFOWIZ where the training provided to you is as per the norms and standards laid down in the Industries and Companies.

Are you looking for the best 6 months big data industrial training in Chandigarh then INFOWIZ is the best option for you. At INFOWIZ we will let you thorough with concepts of Hadoop framework, its ecosystem, Map-reduce, HDFC, PIG, H Base, Flume, etc with the help of which you can easily implement real-life projects in the field of Banking, Insurance, Social Media and E-Commerce on Cloud Lab.

Getting certified along with practical skills, you can make your career achieve greater heights. Our Big Data (Hadoop) course is designed to impart in-depth knowledge of various tools and frameworks so you can easily execute Industry based real-life projects. So opt for the best six months/weeks big data(Hadoop) training in Punjab and give wings to your career growth.

Course Content:

• Understanding on Big Data
• What is Big Data
• Data Facts
• Aspects / Principle of Big Data
• Difference b/w Big Data & Traditional BI
• Examples: Where to use Big Data
• Big Data Business Opportunities
• Distributed File System computation with Facebook Example
• Understanding on LINUX
• Understanding File System working
• Basic commands of LINUX
• Shell scripting
• Use Cases – Assignments
• Understanding of JAVA
• Introduction to OOP’s concept
• Understanding on Data types
• Functions
• Methods
• Setup of Eclipse
• Coding examples
• Use Cases : Assignments
• Understanding of Python
• Concepts of Python
• Data Types in Python
• Exception Handling in Python
• File Handling in Python
• FLUME
• Introduction to Flume
• Setup of Flume Components
• Source
• Sink
• Channel
• Agents
• Use Cases – Assignments
• SPARK
• Introduction to SPARK
• Understanding of RDD, Contexts
• Developing Application in SPARK
• Use Cases – Assignments
• Hadoop 1.x Architecture
• Understanding Hadoop Architect
• Basic Understanding of Hadoop core components
• In depth understanding of HDFS
• Understanding HDFS services – NameNode & DataNode
• Understanding on File System Read & Write
• Real-time Cluster setup based on requirement
• Hadoop 2.x Architecture
• Understanding YARN Architect
• Architect Difference b/w Hadoop 1.x & Hadoop 2.x
• Understanding File System Read & Write
• Cluster Installation
• Hadoop 1.x
• Environment Settings
• Pseudo Mode Installation
• Distributed Mode Installation
• Basic configuration of Hadoop properties
• Understanding in-built scripts
• Running Basic Map Reduce code
• Hadoop 2.x
• Environment Settings
• Distributed Mode Installation
• Configuration of Hadoop properties
• Running Basic Map Reduce Code
• Hadoop File system commands
• SQOOP
• Introduction to Sqoop
• Setup of Sqoop
• Sqoop Import commands
• Sqoop Export commands
• Formats in Sqoop
• Use Cases – Assignments
• PIG – Data Flow Language
• Introduction to PIG Latin
• Setup of Pig
• Independent Mode
• Map Reduce Mode
• Basic commands in Pig
• Functions in Pig
• Developing UDF’s in Java
• Use Cases – Assignments
• Understanding of Map Reduce
• Understanding of Map Reduce services – JobTracker & TaskTracker
• Map Reduce Flow Chart
• Map Reduce Phases
• Mapper
• Reducer
• Splitting
• Sorting
• Shuffling
• Combiner
• Partitioning
• Developing Map Reduce applications – JAVA Code
• Developing Map Reduce applications – Python Code
• Discussion on Input File Formats
• Difference b/w Old MR API & New MR API
• Use Cases – Assignments
• HIVE – Data Warehouse
• Introduction to HIVE Architecture
• Setup of Hive
• Basic queries in HIVE
• Advance Features of HIVE
• Partitioning
• Bucketing
• Serialize & De-serialize
• Query optimization in Hive
• Use Cases – Assignments