Hadoop Online Training In India


Hadoop Online Training in India by Hadoop Certified Professional Contact him — trainer.hadoop@gmail.com –call him @+91 9949514010

Hadoop Online Training in India by real time Experts with live project call at +91 9949514010 Online Hadoop Training classes with real time scenarios.Hadoop is framework for running applications on large clusters built of commodity hardware.Hadoop architecture provides both reliability and availability.It is opensource distributed processing with highly scalable system.system. Hadoop key features are flexible,economical,scalable and reliable.Hadoop is highly scalable and efficient falut tolerent system.Hadoop follows programming design called “Map/Reduce”,where your application logic is spread across the large set of clusters. Hadoop follows a file system called HDFS means Hadoop distributed file system.In HDFS ,each block is 64 MB in size.Data will be divided in blocks.Blocks will be replicated 3 times by default.one can change default replication by configuration settings in hadoop.In Hadoop distributed file system,data will be replicated multiple times to get high availability of the data which is available in servers.HDFS handles the node failures automatically and data will be replicated on available nodes.Pig,Hive and sqoop are the different types of the programming ways,in which we can access the hadoop file system. Hadoop real benefit will be only knows when we work with TB’s of data,because RDBMS takes couple of hours when it is processing the Terabytes of data,hadoop will does same work in couple of minutes.Hadoop has master and slave architecture for both storage and processing.Main components of the HDFS is namenode,datanode and secondary name node.The main components of mapreduce is job tracker and task tracker.Biggest code contributors of hadoop is “Hortonworks with approximately 19% and cloudera with 16% and 14% yahoo and remaining code contributions will be contributed by other companies such as microsoft ebay and mapr.Hadoop mainly used for log processing,search,recommendation system and image analysis.Currently hadoop is used by Facebook,amazon,ebay and so many big companies.

Hadoop Online Training in  India

Course Curriculum:

1.  Understanding Big Data and Hadoop

Learning Objectives –  In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, the common Hadoop ecosystem components, Hadoop Architecture, HDFS, Anatomy of File Write and Read, Rack Awareness.

We will discuss these below topics as part of Big Data,hadoop online training

Big Data, Limitations and Solutions of existing Data Analytics Architecture, Hadoop, 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 MapReduce Framework – I

Learning Objectives – In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will learn about YARN concepts in MapReduce.
We will discuss these below topics as part of MapReduce,hadoop online training
  1. MapReduce Use Cases,
  2. Traditional way Vs MapReduce way,
  3. Why MapReduce,
  4. Hadoop 2.x MapReduce Architecture,
  5. Hadoop 2.x MapReduce Components,
  6. YARN MR Application Execution Flow,
  7. YARN Workflow,
  8. Anatomy of MapReduce Program,
  9. Demo on MapReduce.

3.  Hadoop MapReduce Framework – II

Learning Objectives – In this module, you will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets.
We will discuss these below topics as part of MapReduce,hadoop online training
  1. Input Splits,
  2. Relation between Input Splits and HDFS Blocks,
  3. MapReduce Job Submission Flow,
  4. Demo of Input Splits,
  5. MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set, Demo on Weather Data set.

4.  Advance MapReduce

Learning Objectives – In this module, you will learn Advance MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and how to deal with complex MapReduce programs.
We will discuss these below topics as part of MapReduce,hadoop online training
  1. Counters,
  2. Distributed Cache,
  3. MRunit,
  4. Reduce Join,
  5. Custom Input Format,
  6. Sequence Input Format.

5.  Pig

Learning Objectives – In this module, you will learn Pig, types of use case we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting.
We will discuss these below topics as part of Pig,hadoop online training
  1. About Pig,
  2. MapReduce Vs Pig,
  3. Pig Use Cases,
  4. Programming Structure in Pig,
  5. Pig Running Modes,
  6. Pig components,
  7. Pig Execution,
  8. Pig Latin Program,
  9. Data Models in Pig,
  10. Pig Data Types.
  11. Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF, Pig Demo on Healthcare Data set.

6.  Hive

Learning Objectives – This module will help you in understanding Hive concepts, Loading and Querying Data in Hive and Hive UDF. 
We will discuss these below topics as part of Hive,hadoop online training
  1. Hive Background,
  2. Hive Use Case,
  3. About Hive,
  4. Hive Vs Pig,
  5. Hive Architecture and Components,
  6. Metastore in Hive,
  7. Limitations of Hive,
  8. Comparison with Traditional Database,
  9. Hive Data Types and Data Models,
  10. Partitions and Buckets,
  11. Hive Tables(Managed Tables and External Tables),
  12. Importing Data,
  13. Querying Data,
  14. Managing Outputs,
  15. Hive Script,
  16. Hive UDF,
  17. Hive Demo on Healthcare Data set.

7.  Advance Hive and HBase

Learning Objectives – In this module, you will understand Advance Hive concepts such as UDF, dynamic Partitioning. You will also acquire in-depth knowledge of HBase, Hbase Architecture and its components.
We will discuss these below topics as part of Hive,Hbase and hadoop online training
  1.  Hive QL:
  2. Joining Tables,
  3. Dynamic Partitioning,
  4. Custom Map/Reduce Scripts,
  5. Hive : Thrift Server, User Defined Functions.
  1. Introduction to NoSQL Databases and HBase, HBase v/s RDBMS,
  2. HBase Components,
  3. HBase Architecture,
  4. HBase Cluster Deployment.

8.  Advance HBase

Learning Objectives – This module will cover Advance HBase concepts. We will see demos on Bulk Loading , Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper.
We will discuss these below topics as part of HBase,hadoop online training 
  1.  HBase Data Model
  2. HBase Shell
  3. HBase Client API
  4. Data Loading Techniques
  5. ZooKeeper Data Model
  6. Zookeeper Service
  7. Zookeeper
  8. Demos on Bulk Loading
  9. Getting and Inserting Data
  10. Filters in HBase

9.  Oozie and Hadoop Project

oozie is workflow schedular system to manage apache hadoop jobs.oozie is scalable,reliable and extesible system.oozie is integrated with the rest of the hadoop stack supporting several types of hadoop jobs.Example:-java mapreduce,Streaming map-reduce,Pig,Hive,Sqoop.oozie coordinator jobs are recurrent oozie workflow jobs triggered by time and data availability.It is something like CronTab in Linux or File Polar in Java.Yahoo is running 200k Jobs per day using Oozie.
We will discuss these below topics as part of Oozie,hadoop online training 
  1. Flume and Sqoop Demo
  2. Oozie
  3. Oozie Components
  4. Oozie Workflow
  5. Scheduling with Oozie
  6. Demo on Oozie Workflow
  7. Oozie Co-ordinator
  8. Oozie Commands
  9. Oozie Web Console
  10. Hadoop Project Dem0