HADOOP TRAINING IN CHENNAI

Having thought of becoming a Hadoop engineer?. Then, without further delay, start grabbing the initial impression of the course and the institute that is providing the best training for Hadoop by partnering up with professionals. You can strongly understand the concepts of core components, Database, and Linux operating system which is directly or indirectly associated with the responsibilities of Hadoop engineer. Since it is open-source software with high security so many MNCs with massive data sets over the clusters of machines have been using this simple programming model.
The course structure that you find here is designed by the best professionals who are working with leading MNCs. Since professional Hadoop Developers are involved with your Hadoop Training in Chennai, trainees can easily set you up with real-time projects in all frameworks of Big Data Hadoop. Data Engineering using SQL, NoSQL, Hadoop ecosystem, including most extensively used elements like HDFS, Spark, PySpark, Hive, Sqoop, Impala, and AWS are a few important Big Data training that you will grasp during this session.

About Best Hadoop Training in Chennai Course

What is Best Hadoop Training in Chennai ?

A collective of unmeasured or measured data will be difficult to handle using a traditional processor since most of it will not be in a structured format. Here Hadoop big data helps a lot in structuring data and it doesn’t let any problem occur to the environment despite its complexity. To serve more accuracy in servers and time, Apache has developed a brilliant Big Data Hadoop. It is the only phenomenal open-source Big Data developer software.

Why Learn Hadoop?

If your aim is to get placed at big and successful MNCs, then learning Big Data Hadoop will definitely take you places. Most of the well-versed MNCs are utilizing Hadoop for its accuracy and ease of operation. Even Google, Yahoo, IBM and eBay prefer Hadoop Training in Chennai , which makes it a wonderful choice for you to pick.

Data Production Statistics

  • By 2003, the stock of the data was 6 billion GB.
  • The exact same amount of data was produced every two days in 2011
  • With no exclamatory, the same amount of data was produced every 2 minutes in the year 2013
  • Now think about the present year and future.

Data is Everything in the World

Data is what all companies need. From start-ups to well-developed brands. From mobile to PC, every application and software needs our data for its run. Most of the data will be given by us. For a minute, uncountable data will be shared which is not an easy task to classify.

Learning Outcomes from our Hadoop Course:

  • Professionals of BtreeSystems are highly experienced and will train you for the best.
  • They help you dive deep into the knowledge of Hadoop fundamental concepts.
  • Hadoop Distributed File System (HDFS) and MapReduce concepts will no longer be tougher.
  • Get hands-on experience in Map Reduce Programs and Implementation of HBase. Sessions will be helpful for you to become an expert of yourself.
  • Learn Data loading techniques using Sqoop and Flume.
  • And a predominant certification for Big Data Apache Hadoop.

 

Hadoop Training Highlights

  • Get trained by the experts who are going to help you learn through real-time projects.
  • It is not essential to be brilliant at other traditional Big Data software to understand Hadoop.
  • We start our sessions from scratch.
  • At the end of this course, you will be a professional Hadoop Developer.

Section 1: INTRODUCTION TO BIG DATA-HADOOP
• Overview of Hadoop Ecosystem
• Role of Hadoop in Big data– Overview of other Big Data Systems
• Who is using Hadoop
• Hadoop integrations into Exiting Software Products
• Current Scenario in the Hadoop Ecosystem
• Installation
• Configuration
• Use Cases of Hadoop (HealthCare, Retail, Telecom)

Section 2: HDFS
• Concepts
• Architecture
• Data Flow (File Read, File Write)
• Fault Tolerance
• Shell Commands
• Data Flow Archives
• Coherency – Data Integrity
• Role of Secondary NameNode

Section 3: MapReduce
• Theory
• Data Flow (Map – Shuffle – Reduced)
• MapRed vs MapReduce APIs
• Programming: Mapper, Reducer, Combiner, Partitioner
• Writables
• InputFormat
• Output format
• Streaming API using python
• Inherent Failure Handling using Speculative Execution
• Magic of Shuffle Phase
• FileFormats
• Sequence Files

Section 4: HBASE
• Introduction to NoSQL
• CAP Theorem
• Classification of NoSQL
• Hbase and RDBMS
• HBASE and HDFS
• Architecture (Read Path, Write Path, Compactions, Splits)
• Installation
• Configuration
• Role of Zookeeper
• HBase Shell Introduction to Filters
• RowKeyDesign – What’s New in HBase Hands-On

Section 5: HIVE
• Architecture
• Installation
• Configuration
• Hive vs RDBMS
• Tables
• DDL
• DML
• UDF
• Partitioning
• Bucketing
• Hive functions
• Date functions
• String functions
• Cast function Meta Store

Section 6: PIG
• Architecture
• Installation
• Hive vs Pig
• Pig Latin Syntax
• Data Types
• Functions (Eval, Load/Store, String, DateTime)
• Joins
• UDFs – Performance
• Troubleshooting
• Commonly Used Functions

Section 7: SQOOP
• Architecture, Installation, Commands (Import, Hive-Import, EVal, Hbase Import, Import All Tables, Export)
• Connectors to Existing DBs and DW

Section 8: KAFKA
• Kafka introduction
• Data streaming Introduction
• Producer – Consumer Topics
• Brokers
• Partitions
• Unix Streaming via Kafka

Section 9: OOZIE
• Architecture
• Installation
• Workflow
• Coordinator
• Action (Mapreduce, Hive, Pig, Sqoop)
• Introduction to Bundle
• Mail Notifications

Section 10: HADOOP 2.0 and Spark
• Limitations on Hadoop
• 1.0 – HDFS Federation
• High Availability in HDFS
• HDFS Snapshots
• Other Improvements in HDFS2
• Introduction to YARN aka MR2
• Limitations on MR1
• Architecture of YARN
• MapReduce Job Flow in YARN
• Introduction to Stinger Initiative and Tez
• BackWard Compatibility for Hadoop 1.X
• Spark Fundamentals
• RDD – Sample Scala Program – Spark Streaming

Section 11: Big Data Use cases
• Hadoop
• HDFS architecture and usage
• MapReduce Architecture and real time exercises
• Hadoop Eco systems
• Sqoop – MySQL DB Migration
• Hive. — Deep drive
• Pig – weblog parsing and ETL
• Oozie – Workflow scheduling
• Flume – weblogs ingestion
• No SQL
• HBase
• Apache Kafka
• Pentaho ETL Tool Integration ‘Working with Hadoop Eco System
• Apache SPARK
• Introduction and work with RDD.
• Multinode Setup Guidance
• Hadoop’s latest version of the Pros and Cons discussion
• Ends with an Introduction to Data Science.

Section 12: Real-Time Project
• Get application web logs
• Getting user information from my sql via sqoop
• Getting the extracted data from Pig script
• Creating a Hive SQL Table for querying
• Creating Reports from Hive QL

BTREE SYSTEM – Key Features

Training from
Industrial Experts

Hands on
Practicals/ Projects

100% Placement
Assistance

24 x 7
Expert Support

Certification
of Completion

Free
Live Demo

FREQUENTLY ASKED HADOOP QUESTIONS

Prerequisite to learn Hadoop?

  • It will be effective if you know the concept of Java and Linux
  • Basic knowledge in  Database and SQL
  • It will be appreciated and easy for trainers if you are good at Mathematics and Statics.

What are all the tools you’ll cover in this Hadoop training program?

  • MapReduce
  • Hbase
  • HIVE
  • PIG
  • Sqoop
  • Spark
  • OOZIE

What is the Course Duration for Hadoop Training?

BtreeSystem’s Hadoop Course Duration will be around 45 to 50 hours.

What will be the career path of a Hadoop Developer?

You will never regret the choice of choosing the Hadoop Developer. Both experienced and freshers have a huge scope.

Will you help me with interviews?

YES! We will stand with you for your placement goals.