Apache Hadoop Data Analyst Training

Apache Hadoop Data Analyst Training


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Apache Hadoop Data Analyst Training

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


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
  • 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


ETL Tool (Pig) Introduction Level-1 (Basics)

Pig Introduction


ETL Tool (Pig) Level-2 (Complex)

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


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


Hive Level-2 (Complex)

Hive releases

  • Why Hive is not best solution for OLTP OLAP in Hive
  • Partitioning
  • Bucketing
  • Hive Architecture
  • 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
  • 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


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


NOSQL Databases and Introduction to HBase (2 Hours)

  • 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


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
  • 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
  • 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

Flume , Oozie and YARN

How to load data streaming data without fixe schema


Apache Spark Basics


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

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