Advanced BigData Analyst Training (Hadoop)

Day 1

Module 1 Hadoop Introduction

• Why we need Hadoop
• Why Hadoop is in demand in market now a days
• Where expensive SQL based tools are failing
• Key points , Why Hadoop is leading tool in current It Industry Definition of BigData
• Hadoop nodes
• Introduction to Hadoop Release-1
• Hadoop Daemons in Hadoop Release-1
• Introduction to Hadoop Release-2
• Hadoop Daemons in Hadoop Release-2
• Hadoop Cluster and Racks
• Hadoop Cluster Demo
• New projects on Hadoop
• How Open Source tools is capable to run jobs in lesser time Hadoop Storage – HDFS (Hadoop Distributed file system) Hadoop Processing Framework (Map Reduce / YARN) Alternates of Map Reduce
• Why NOSQL is in much demand instead of SQL
• Distributed warehouse for HDFS
• Hadoop Ecosystem and its usages
• Data import/Export tools

Module 2 : Hadoop Installation and Hands-on on Hadoop machine

• Hadoop installation
• Introduction to Hadoop FS and Processing Environment’s UIs How to read and write files
• Basic Unix commands for Hadoop
• Hadoop FS shell
• Hadoop releases practical
• Hadoop daemons practical

Day 2

Module 3: ETL Tool (Pig) Introduction Level-1 

• Pig Introduction
• Why Pig if Map Reduce is there?
• How Pig is different from Programming languages Pig Data flow Introduction
• How Schema is optional in Pig
• Pig Data types
• Pig Commands – Load, Store , Describe , Dump Map Reduce job started by Pig Commands
• Execution plan

Module 4 :ETL Tool (Pig) Level-2 

• Pig- UDFs
• Pig Use cases
• Pig Assignment
• Complex Use cases on Pig
• Real time scenarios on Pig
• When we should use Pig
• When we shouldn’t use Pig

Day 3

Module 5: Hive Warehouse

• Hive Introduction
• Meta storage and meta store
• Introduction to Derby Database
• Hive Data types
• HQL
• DDL, DML and sub languages of Hive
• Internal , external and Temp tables in Hive
• Differentiation between SQL based Datawarehouse and Hive

Module 6 : Hive Level-2

• Hive releases
• Why Hive is not best solution for OLTP OLAP in Hive
• Partitioning
• Bucketing
• Hive Architecture
• Thrift Server
• Hue Interface for Hive
• How to analyze data using Hive script Differentiation between Hive and Impala UDFs in Hive
• Complex Use cases in Hive
• Hive Advanced Assignment

Module 7: Introduction to Map Reduce

• How Map Reduce works as Processing Framework End to End execution flow of Map Reduce job Different tasks in Map Reduce job
• Why Reducer is optional while Mapper is mandatory? Introduction to Combiner
• Introduction to Partitioner
• Programming languages for Map Reduce
• Why Java is preferred for Map Reduce programming

Module 8 : NOSQL Databases and Introduction to HBase

• Introduction to NOSQL
• Why NOSQL if SQL is in market since several years
• Databases in market based on NOSQL
• CAP Theorem
• ACID Vs. CAP
• OLTP Solutions with different capabilities
• Which Nosql based solution is capable to handle specific requirements Examples of companies that uses NOSQL based databases
• HBase Architecture of column families

Day 4

Module 9: Zookeeper and SQOOP

• Introduction to Zookeeper
• How Zookeeper helps in Hadoop Ecosystem
• How to load data from Relational storage in Hadoop Sqoop basics
• Sqoop practical implementation
• Sqoop alternative
• Sqoop connector

Module 9 : Flume , Oozie and YARN

• How to load data streaming data without fixe schema
• How to load unstructured and semi structured data in Hadoop Introduction to Flume
• Hands-on on Flume
• How to load Twitter data in HDFS using Hadoop
• Introduction to Oozie
• How to schedule jobs using Oozie
• What kind of jobs can be scheduled using Oozie
• How to schedule jobs which are time based
• Hadoop releases
• From where to get Hadoop and other components to install
• Introduction to YARN
• Significance of YARN

Module 10 : Apache Spark Basics 

• Introduction to Spark
• Basics Features of SPARK and Scala available in Hue Why Spark demand is increasing in market
• How can we use Spark with Hadoop Eco System Datasets for practice purpose

Module 11 : Emerging Trends of Big Data

• YARN
• Emerging Technologies of Big Data
• Emerging use cases e.g IoT, Industrial Internet, New Applications
• Certifications and
• Job Opportunities