BIG DATA - HADOOP
FOR PROFESSIONALS
Big Data - Hadoop
Course Overview
Description:
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Data scientists build information platforms to provide deep insight and answer previously unimaginable questions.Hadoop are transforming how data scientists work by allowing interactive and iterative data analysis at scale.Learn how Hadoop enable data scientists to help companies reduce costs, increase profits, improve products,retain customers, and identify new opportunities. This course helps participants understand what data scientists do, the problems they solve, and the tools and techniques they use. Through in-class simulations, participants apply data science methods to real-world challenges in different industries and, ultimately, prepare for data scientist roles in the field. scientist roles in the field.
Expectations and Goals:
Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, and develop concrete skills such as:
How to identify potential business use cases where data science can provide impactful results?
How to obtain, clean and combine disparate data sources to create a coherent picture for analysis?
What statistical methods to leverage for data exploration that will provide critical insight into your data?
Introduction to Hive
Why we need Hive
Architecture of Hive
Hive Data Types
Hive Complex Datatypes
Managed Tables
External Tables
Running Hive Queries
Perform Joining
Handling JSON Data
Handling XML Data
Partitioned Table
Hive UDF
Scripting in Hive
Performance Tuning in Hive
Case Study in Hive based on Dataset
Module 3:
Introduction to Pig
Why we need Pig Technology
Architecture of Pig
Pig Data Types
Module 4:
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Concept of Impala
Running the Queries on Impala
Compare Impala with Hive
Concept of HUE
Access The Hadoop Component by means of HUE
Module 8:
Introduction to NOSQL Database
Compare between NOSQL and RDBMS
Introduction to HBASE
Why we need HBASE
HBASE Commands
Module 9:
Installation of Tableau
Communicate Tableau with impala
Plotting the Graph
Module 10:
Module 11:
Project Work and Documentation
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Prerequesites:
Object Oriented Programming in Java, Exception in Java
Knowledge of SQL Command
Basic Command in Linux
Topics Covered:
Where and when to leverage Hadoop streaming and Apache Flume for data science pipelines? Whatmachine learning technique to use for a particular data science project?
Introduction to Big Data
Features of Hadoop
Components in Hadoop
Concept of Hadoop Ecosystem
Introduction to HDFS
HDFS Practical
Module 1:
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Collection Framework in Java (List, Map, Iterator)
String Tokenizer, File Handling, String Handling
Concept of Map Reduce
Map Reduce Practical
Module 2:
Different Modes in Pig
Running Pig Command
Tuple,Bag,Map
Pig UDF
Script in Pig
Case Study in Pig Based on Dataset
Introduction to SQOOP
Importing and Exporting the RDBMS to HDFS
Import data from RDBMS to Hive
Export Data from Hive to RDBMS
Assignment
Module 5:
Introduction to Flume
Introduction Source,Sink,Flume Agents
Fetching Twitter Data into Solr
Configuration to create twitter data into HDFS
Use HiveSerde to Analyze the data
Module 6:
Sentiment Analysis based on Twitter Data
Module 7:
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Thank You