The Big Data and Hadoop training is designed to provide skills, knack and knowledge to become the finest and expertise Hadoop developer. It provides the learner with in-depth and thorough knowledge of concepts such as HDFS, Hadoop Architecture, HIVE, Flume, HBase, Map-Reduce, Oozie, Big Insights, Sqoop, PIG, Impala, JAQL, Administering Hadoop Cluster etc. At the end of the training the learner would get prone to practical exposure and could gain hands-on experience in the topics covered.
The Big Data and Hadoop training is designed to provide skills, knack and knowledge to become the finest and expertise Hadoop developer. It provides the learner with in-depth and thorough knowledge of concepts such as HDFS, Hadoop Architecture, HIVE, Flume, HBase, Map-Reduce, Oozie, Big Insights, Sqoop, PIG, Impala, JAQL, Administering Hadoop Cluster etc. At the end of the training the learner would get prone to practical exposure and could gain hands-on experience in the topics covered.The learner may start with no knowledge in course of “Hadoop” and this training would not only help them, to “learn” and “setup” Hadoop but also helps one to clear the certification level examination at fraction of cost.
Soon after the successful completion of the 'Big Data and Hadoop' Course the learner could be able to excel in the following topics:
Who should go for this course?
This course is mainly designed for professionals who aspire to make their career graph in Big Data Analytics using Hadoop Framework. The key beneficiaries of this course includes Project Managers, Analytic Professionals, Testing Professionals, Software Professionals, ETL developers etc.. But not only these it also includes some other professionals who looks forward to acquire the solid foundation can opt this course with no other second thought.
The pre-requisites which the learners have to possess, in order to learn Hadoop is to have the basic knowledge in “Core& Advanced Java Concepts” and also the analytical abilities through which they can easily grasp and apply the concepts essence in Hadoop. In addition to this we do offer special program namely ““Java Essentials for Hadoop” for all the participants who ever enrolls with us for Hadoop course to brush up their Java skills which ever needed to write and monitor the programs in case of “Map Reduce”.
Why learn Hadoop?
Many of us have a common doubt that why to learn Hadoop and why is it so important? In this fast and emerging Internet world as we all know that the wide range of varieties/volumes of data grow each and every day, especially from automated sensors and Social Media… so how to handle this?????
One of the best solution for this is: “Hadoop” where it can handle, the large amounts of the data, i.e.(the data can be of any kind) in a quicker and easier way.
And let us consider few more factors:
To store huge quantities of data Hadoop makes use of “open-source framework” which is of free of cost and it even uses “distributed computing model” through which it can quickly process the data (high volumes).
Only with Little administration the user can easily grow their system by simply adding more nodes to it.
Unlike the traditional relational databases, there is no need for the user to preprocess the data and to store, include unstructured data like text, images, videos by Hadoop.
Inherent data protection and self-healing capabilities:
The processing of applications and the data seems protected against the hardware failure. If at all the node goes down, then the jobs gets automatically redirected towards other nodes in order to ensure that the distributed computing does not fail at all. Hadoop is an open-source software framework which helps to store and process the big data in a distributed fashion. Essentially, it mainly helps to perform two tasks:
a. Faster processing.
b. Massive data storage
Opportunities for Hadoopers!
Opportunities for Hadoopers seems infinite – i.e. from Hadoop Developer, to the Hadoop Tester/ Hadoop Architect, and so on.
If at all the cracking and managing of Big Data is passion in life, then think ahead and Join Hadoop Online course and carve a niche for yourself!
1.Understanding Big Data and Hadoop
Learning Objectives - In this module, learner will understand the Big Data, how Hadoop could solve the Big Data problems, the limitations, Hadoop Architecture, the common Hadoop ecosystem components, Anatomy of File, HDFS, Rack Awareness, Write and Read etc..
• Big Data
• Limitations and Solutions of existing Data Analytics Architecture
• Hadoop Features
• Hadoop Ecosystem
• Hadoop 2.x core components
• Hadoop Storage: HDFS
• Hadoop Processing: MapReduce Framework,
• Anatomy of File Write and Read,
• Rack Awareness.
2. Hadoop Architecture and HDFS
Learning Objectives - In this module, the learner will come across the Hadoop Cluster Architecture, Data Loading Techniques and Important Configuration files in the Hadoop Cluster,
• Hadoop 2.x Cluster Architecture
• Hadoop Cluster Modes,
• Federation and High Availability
• Hadoop 2.x Configuration Files
• A Typical Production Hadoop Cluster
• MapReduce Job Execution
• Data Loading Techniques: Hadoop Copy Commands
• Common Hadoop Shell Commands
• Password-Less SSH
3. Hadoop MapReduce Framework - I
Learning Objectives - In this module, the learner will understand the Hadoop MapReduce framework where the data is been stored in HDFS and even the YARN concepts in MapReduce.
• Traditional way Vs MapReduce way
• Why MapReduce
• MapReduce Use Cases
• Hadoop 2.x (MapReduce )Architecture
• Hadoop 2.x (MapReduce ) Components
• YARN MR Application Execution Flow
• YARN Workflow
• Demo on MapReduce.
• Anatomy of MapReduce Program
4. Hadoop MapReduce Framework - II
Learning Objectives - In this section learner will understand the concepts Combiner & Partitioner, Input Splits in MapReduce, Demos on MapReduce by using different data sets.
• Input Splits
• Relation between HDFS Blocks and Input Splits
• Submission Flow of Map Reduce Job
• Demo of Input Splits
• Map Reduce:
• Combiner & Partitioner,
• Demo on de-identifying the Health Care Data set
• Demo on Weather Data set.
5. Advance Map Reduce
Learning Objectives - In this module, the learner do understand the Advance MapReduce concepts such as Distributed Cache, Counters, MR unit, Custom Input Format, Reduce Join, Sequence Input Format and to deal with the complex Map Reduce programs.
• Distributed Cache
• Custom Input Format
• Reduce Join
• Sequence Input Format.
Learning Objectives - In this module, the learner would learn Pig, types of use cases - Pig, tight coupling between Map Reduce & Pig and the Pig Latin scripting.
• About Pig,
• MapReduce Vs Pig,
• Pig Use Cases,
• Pig components,
• Programming Structure in Pig,
• Pig Execution,
• Pig Running Modes,
• Data Models in Pig,
• Pig Data Types.
• Pig Latin Program,
• Pig Latin :
• Relational Operators
• Group Operator
• File Loaders
• COGROUP Operator
• Joins and COGROUP
• Diagnostic Operators
• Pig UDF
• Pig Demo - Healthcare Data set.
Learning Objectives - This module helps in understanding the Hive concepts, Loading, Querying the Data in Hive and Hive UDF.
• About Hive
• Hive Architecture and Components
• Hive Data Types and Data Models
• Hive Use Case
• Hive Background
• Metastore in Hive
• Limitations of Hive
• Hive Vs Pig
• Partitions and Buckets
• Comparison with Traditional Database
• Hive Tables (Managed Tables and External Tables)
• Querying Data
• Importing Data
• Managing Outputs
• Hive UDF
• Hive Script
• Hive Demo -Healthcare Data set.
8. Advance Hive and HBase
Learning Objectives - In this module, the learner will understand the Advance Hive concepts such as dynamic Partitioning and UDF, what is HBase, Hbase Architecture and all its components.
• Joining Tables
• Custom Map/Reduce Scripts,
• Dynamic Partitioning
• User Defined Functions.
• Thrift Server,
• Introduction to NoSQL Databases HBase,
• HBase v/s RDBMS
• HBase Cluster Deployment.
• HBase Components
• HBase Architecture
9. Advanced HBase
Learning Objectives - This module helps to cover the advanced HBase concepts, Filters and Bulk Loading. And also what the Zookeeper is all about and how it helps to monitor the cluster etc...
HBase Data Model
HBase Client API
ZooKeeper Data Model
Data Loading Techniques
Getting and Inserting Data
Demos on Bulk Loading
Filters in HBase.
10. Oozie and Hadoop Project
Learning Objectives :
In this module, the learner could understand the working of multiple Hadoop ecosystem components altogether in the implementation of Hadoop to solve Big Data problems.
One could discuss the multiple data sets and its project significance. This module also helps to cover Sqoop and Flume demo and even the Apache Oozie Workflow Scheduler - Hadoop Jobs.
• Flume and Sqoop Demo
• Oozie Workflow
• Scheduling with Oozie
• Demo on Oozie Workflow
• Oozie Commands
• Hadoop Project Demo.
• Oozie Components
• Oozie Co-ordinator
• Oozie Web Console
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