Certificate in Advanced MS Excel
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Big Data Analytics using Hadoop
Lucrative Career Option73% of organizations have already invested or plan to invest in big data.
Job OpportunitiesComplete the Hadoop online training become eligible for over 1,500,000 jobs.
Who Should AttendBeginners in analytics domain, engineering & non-engineering graduates.
Salary PackagesThe average salary for an entry-level Big Data engineer is ₹5 lakhs per annum.
Expert FacultyThe Big Data certification course is taught by eminent big data experts.
Are you interested in increasing your knowledge in the Big Data landscape and becoming a certified Big Data professional? Most modern businesses now have access to massive amounts of data which they are extensively using to guide their decision-making process. And Hadoop, one of the most popular open-source Big Data framework has made big data analysis easier and more accessible - increasing its potential to transform the world!
Manipal ProLearn’s course in Big Data Analytics using Hadoop will help you to understand the A to Z of Big Data and Hadoop analytics. You’ll also be able to go into the depth of other advanced Big Data technologies such as SPARK, Storm, and Kafka etc. A complete understanding of these technologies would enable you to work as a Big Data engineer and explore the world of analytics & data sciences.
After completing the Big Data Analytics using Hadoop online training, you’ll be considered as a strong and competent Big Data engineer. The big data analytics with Hadoop course will help you to:
- Understand what is Big Data and the sources of Big Data.
- Appreciate the need for a platform like Hadoop.
- Overview of the technological landscape of Big Data.
- Demonstrate the ability to quickly perform ad-hoc analysis of Big Data (structured and unstructured).
- Overcome the limitations of standalone tools like Excel & R.
Big Data Analytics aspirants
Beginners in the analytics domain
Challenges of Processing Big Data
History of Hadoop
Ecosystem of Hadoop
HDFS and Map Reduce Paradigm
Big Data Technologies
Features of Hadoop
Hadoop Installation and Configuration
Hive Installation and Configuration
Pig Installation and Configuration
Sqoop Installation and Configuration
Oozie Installation and Configuration
Flume Installation and Configuration
Hbase Installation and Configuration
Hue Installation and Configuration
HDFS Configuration Files
Data Storage in HDFS
Blocks and Splits
Name Node – Demo
HDFS Data Storage – Demo
Data Replication – Demo
Reliable Storage – Demo
Data Node – Demo
HDFS Clients – Demo
HDFS Clients – Demo
Basic HDFS Commands - Demo
Read Anatomy in HDFS
Write Anatomy in HDFS
Additional HDFS Commands - Demo
HDFS File System API
HDFS File System API - Demo
HDFS Permission Management
Permission Management – Demo
MapReduce 1 Architecture
MR and Traditional Approach
Introduction to YARN
Executing a MapReduce Program - Demo
Datatypes and APIs
Mapper – Demo
MapReduce – Demo
Combiners – Demo
Partitioners – Demo
Debug logs & Printing in MR Jobs – Demo
Path Filters – Demo
Splits – Demo
Named Output – Demo Summary
Write MapReduce Keys and Values – Demo
Identity Mappers – Demo
Identity Reducers – Demo
Counters in Hadoop
MapReduce Counters – Demo
Input and Output Formats
About MR Unit
MR Unit – Demo
Map Task Execution
Sort and Shuffle
Reduce Task Execution
Job Failure – Demo
Staggering Job – Demo
Uses of Serialization
Introduction - Compression
Uses of Compression
Customization of Input Format APIs
Input Format and Record Readers – Demo
Distributed Cache – Demo
Map Side Joins
Map Side Joins – Demo
Reduce Side Joins
Reduce Side Joins – Demo
Sequence File Format
Sequence File Creation – Demo
Sequence File with MapReduce -Demo
Hadoop Streaming – Demo
Configuring Development Environment using Eclipse – Demo
Running MapReduce Jobs – Demo
Hive vs RDBMS
Hive Schema Model
Hive Integration with Hadoop
Hive Query Language
Transformation in Hive
Hive Database Creation - Demo
Hive Tables - Demo
Hive Queries - Demo
Advanced Hive Partitioning - Demo
Bucketing - Demo
Advanced Concepts - Demo
Manage an XML or JSON files - Demo
Use a predefined SERDE - Demo
MapReduce and Pig
Modes of Execution in Pig
Pig Datatypes and Operators
SQL vs Apache Pig
Loading Data in Pig – Demo
Pig Dialects – Demo
Transformations in Pig – Demo
Debugging in Pig – Demo
Other capabilities in Pig - Demo
Categories of NoSQL Databases
Hbase vs RDBMS
Basic CRUD Operation Demo
Basic CRUD Operation
Introduction - Apache Sqoop
Working with Sqoop – Demo
Hive Integration – Demo
Hbase Integration – Demo
Sqoop Scripts - Demo
Introduction - Apache Oozie
Basic Workflow Setup
Types of Oozie Actions
Defining a Workflow
Run MapReduce with Oozie - Demo
Introduction - Apache Hue
Hue User Interface
Working with Hive using Hue
Working with Pig using Hue
Monitoring an Oozie Job using Hue
Apache Hue – Demo
Apache Flume Introduction
Flume Core Components
Apache Flume - Demo
Apache Spark & Storm Introduction
Spark Streaming Concepts
Introduction - Real time deployment
Logical deployment overview
Physical deployment overview
Introduction - Big data software and tools
Other ecosystem tools
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