Big Data and Hadoop Training in Delhi Noida NCR
About The Course
The Big Data and Hadoop training course from Teras is designed to enhance your knowledge and skills to become a successful Hadoop developer. In-depth
knowledge of core concepts will be covered in the course along with implementation on varied industry use-cases.
By the end of the course, you will:
1. Master the concepts of HDFS and Map Reduce framework
2. Understand Hadoop 2.x Architecture
3. Setup Hadoop Cluster and write Complex Map Reduce programs
4. Learn data loading techniques using Sqoop and Flume
5. Perform data analytics using Pig, Hive and YARN
6. Implement HBase and MapReduce integration
7. Implement Advanced Usage and Indexing
8. Schedule jobs using Oozie
Who should go for this course?
Today, Hadoop has become a foundation stone of every business technology professional. To stay ahead , Hadoop has become a must-know technology for the
1. Analytics professionals
2. BI /ETL/DW professionals
3. Project managers
4. Testing professionals
5. Software developers and architects
6. Graduates aiming to build a successful career around Big Data
1. Understanding Big Data and Hadoop
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 Architecture and HDFS
Hadoop 2.x Cluster Architecture - Federation and High Availability, A Typical Production Hadoop Cluster, Hadoop Cluster Modes, Common Hadoop Shell
Commands, Hadoop 2.x Configuration Files, Password-Less SSH, MapReduce Job Execution, Data Loading Techniques: Hadoop Copy Commands, FLUME, SQOOP.
3. Hadoop MapReduce Framework - I
MapReduce Use Cases, Traditional way Vs MapReduce way, Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components, YARN MR
Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program, Demo on MapReduce.
4. Hadoop MapReduce Framework - II
Input Splits, Relation between Input Splits and HDFS Blocks, MapReduce Job Submission Flow, Demo of Input Splits, MapReduce: Combiner & Partitioner,
Demo on de-identifying Health Care Data set, Demo on Weather Data set.
5. Advanced MapReduce
Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format.
About Pig, MapReduce Vs Pig, Pig Use Cases, Programming Structure in Pig, Pig Running Modes, Pig components, Pig Execution, Pig Latin Program, Data Models
in Pig, Pig Data Types.
Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF, Pig Demo on
Healthcare Data set.
Hive Background, Hive Use Case, About Hive, Hive Vs Pig, Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with
Traditional Database, Hive Data Types and Data Models, Partitions and Buckets, Hive Tables(Managed Tables and External Tables), Importing Data, Querying
Data, Managing Outputs, Hive Script, Hive UDF, Hive Demo on Healthcare Data set.
8. Advanced Hive and HBase
Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts, Hive : Thrift Server, User Defined Functions.
HBase: Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, HBase Cluster Deployment.
9. Advanced HBase
HBase Data Model, HBase Shell, HBase Client API, Data Loading Techniques, ZooKeeper Data Model, Zookeeper Service, Zookeeper, Demos on Bulk Loading,
Getting and Inserting Data, Filters in HBase.
10. Oozie and Hadoop Project
Flume and Sqoop Demo, Oozie, Oozie Components, Oozie Workflow, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands, Oozie Web
Console, Hadoop Project Demo.