Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. This brief tutorial provides a quick introduction to Big Data, Map Reduce algorithm, and Hadoop Distributed File System.
After completing the Apache Hadoop training, you will be able to:
- Master the concepts of HDFS and MapReduce framework.
- Understand Hadoop 2.x Architecture.
- Setup Hadoop Cluster and write Complex MapReduce programs.
- Learn data loading techniques using Sqoop and Flume.
- Perform data analytics using Pig, Hive.
- Implement HBase and MapReduce integration.
- Schedule jobs using Oozie.
- Implement best practices for Hadoop development
- Developers and Architects
- BI /ETL/DW professionals
- Senior IT Professionals
- Testing professionals
- Mainframe professionals
- Knowledge of Core Java and SQL will be beneficial, but certainly not a mandate.
- If you wish to brush-up Core-Java skills, we provide complementary core java sessions.
- Introduction to BigData, Hadoop
- Deep drive in HDFS
- Single node cluster setup
- Map reduce by using java
- Apache Pig
- Apache Hive
- Apache Hbase
- Apache Flume
- Real time Big data project
|Introduction to BigData, Hadoop|
|Deep drive in HDFS|
|Single node cluster setup|
|Map reduce by using java|
|Real time Big data project|
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