Big Data and Hadoop

IIHT - Kalkaji
In New Delhi

Rs10001-20000
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Important information

  • Training
  • Advanced
  • New delhi
  • When:
    Flexible
Description

Complete Practical knowledge,able to get job after course
Job Roles are-

Hadoop Adminsitrator
Business Analyst

Important information
Venues

Where and when

Starts Location Timetable
Flexible
New Delhi
IIHT, E-153,2nd floor, Kalkaji, Near KFC, New Delhi-110019. D: +91 11 49064172 | www.iiht.com, 110019, Delhi, India
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Frequent Asked Questions

· Qualification

Graduate in any Stream

What you'll learn on the course

Hadoop S.No Course Outline 1 Java Fundamentals 1.1 Basic java concepts 1.2 Multi-threading 1.3 File I/O –Java. IO 1.4 Collections –Java.Util.*
Java.Math
Java.Lang 1.5 Java Generics 1.6 Java Serialization 1.7 Java Database Connectivity –JDBC 1.8 Java Common Design Patterns 1.9 Java Open Source Frameworks (Spring
Apache Maven
Logging
Etc...) 1.10 Java Apache Hadoop Frameworks (Hadoop Common
Map Reduce etc.) 1.11 Understand Web Servers & Application Servers - JBoss Application server
Apache Tomcat server 1.12 Java Unit testing Frameworks (Junit / TestNG) 1.13 Eclipse IDE – Java Development. 1.14 Version Control – GIT
SVN
Etc. 1.15 Java Continuous Integration frameworks – Husdson
Jenkins
Etc. 1.16 Handling XML and XSD using Java frameworks 1.17 Java XML Parsers frameworks – DOM and SAX 1.18 Java Web services concepts – SOA
SOAP
XML
JAXB
1.19 SOAP Web services 1.20 REST web services 2 Hadoop Fundamentals 2.1 What is Big Data? Why Big Data? 2.2 Hadoop Architecture & Components 2.3 Hadoop Storage & File Formats (ASCII
Avro
Parquet
RC4
JSON
EBCDIC etc.) 2.4 Hadoop Processing – Map Reduce
Spark Frameworks 3 Map Reduce 3.1 What Is MapReduce? 3.2 Basic MapReduce Concepts 3.3 Concepts of Mappers
Reducers
Combiners and Paritioning 3.4 Inputs and Output formats to MR Program 3.5 Error Handling and creating UDFs for MR 4 Spark 4.1 What Is Spark? 4.2 Basic Spark Concepts 4.3 How Spark differs from Map Reduce? 4.4 Working with RDD’s 4.5 Parallel Programming wi
Why we need it and its importance in DWH? 5.2 How Hive is different from Traditional RDBMS 5.3 Modeling in Hive
Creating Hive structures and data load process. 5.4 Concepts of Partitioning
Bucketing
Blocks
Hashing
External Tables etc. 5.5 Concepts of serialization
Deserialization 5.6 Different Hive data storage formats including ORC
RC
And Parquet. 5.7 Introduction ton HiveQL and examples. 5.8 Hive as an ELT tool and difference between Pig and Hive 5.9 Performance tuning opportunities in Hive
Learnings and Best Practices. 5.10 Writing and mastering Hive UDFs 5.11 Error Handling and scope of creating Hive UDFs. 6 Pig and Latin 6.1 Basics of Pig and Why Pig? 6.2 Grunt 6.3 Pig’s Data Model 6.4 Writing Evaluation 6.5 Filter 6.6 Load & Store Functi

Teachers and trainers (1)

Pulak Kumar Palit
Pulak Kumar Palit
Hadoop Expert

Course programme

Hadoop S.No Course Outline 1 Java Fundamentals 1.1 Basic java concepts 1.2 Multi-threading 1.3 File I/O –Java. IO 1.4 Collections –Java.Util.*, Java.Math, Java.Lang 1.5 Java Generics 1.6 Java Serialization 1.7 Java Database Connectivity –JDBC 1.8 Java Common Design Patterns 1.9 Java Open Source Frameworks (Spring, Apache Maven, Logging, etc...) 1.10 Java Apache Hadoop Frameworks (Hadoop Common, Map Reduce etc.) 1.11 Understand Web Servers & Application Servers - JBoss Application server, Apache Tomcat server 1.12 Java Unit testing Frameworks (Junit / TestNG) 1.13 Eclipse IDE – Java Development. 1.14 Version Control – GIT, SVN, etc. 1.15 Java Continuous Integration frameworks – Husdson, Jenkins, etc. 1.16 Handling XML and XSD using Java frameworks 1.17 Java XML Parsers frameworks – DOM and SAX 1.18 Java Web services concepts – SOA, SOAP, XML, JAXB, 1.19 SOAP Web services 1.20 REST web services 2 Hadoop Fundamentals 2.1 What is Big Data? Why Big Data? 2.2 Hadoop Architecture & Components 2.3 Hadoop Storage & File Formats (ASCII, Avro, Parquet, RC4, JSON, EBCDIC etc.) 2.4 Hadoop Processing – Map Reduce, Spark Frameworks 3 Map Reduce 3.1 What Is MapReduce? 3.2 Basic MapReduce Concepts 3.3 Concepts of Mappers, Reducers, Combiners and Paritioning 3.4 Inputs and Output formats to MR Program 3.5 Error Handling and creating UDFs for MR 4 Spark 4.1 What Is Spark? 4.2 Basic Spark Concepts 4.3 How Spark differs from Map Reduce? 4.4 Working with RDD’s 4.5 Parallel Programming with Spark 4.6 Spark Streaming 5 Hive 5.1 What is Hive, why we need it and its importance in DWH? 5.2 How Hive is different from Traditional RDBMS 5.3 Modeling in Hive, creating Hive structures and data load process. 5.4 Concepts of Partitioning, Bucketing, Blocks, Hashing, External Tables etc. 5.5 Concepts of serialization, deserialization 5.6 Different Hive data storage formats including ORC, RC, and Parquet. 5.7 Introduction ton HiveQL and examples. 5.8 Hive as an ELT tool and difference between Pig and Hive 5.9 Performance tuning opportunities in Hive, learnings and Best Practices. 5.10 Writing and mastering Hive UDFs 5.11 Error Handling and scope of creating Hive UDFs. 6 Pig and Latin 6.1 Basics of Pig and Why Pig? 6.2 Grunt 6.3 Pig’s Data Model 6.4 Writing Evaluation 6.5 Filter 6.6 Load & Store Functions 6.7 Benefits of Pig over SQL language 6.8 Input and Output formats to MR program. 6.9 Error Handling and scope of creating UDFs for Pig. 7 HBase 7.1 HBase – Introduction 7.2 When to use HBase 7.3 HBase Data Model 7.4 HBase Families & Components 7.5 Data Storage and Distribution 7.6 HBase Master 8 MongoDB 8.1 Introduction to In-Memory Computing 8.2 When to use MongoDB 8.3 MongoDB API 8.4 Indexing and Data Modeling 8.5 Drivers / Replication / Sharding 9 ETL / ELT Solutions Build Workshop 9.1 Java 9.2 MapReduce 9.3 Pig 9.4 Hive 9.5 HBase 9.6 Cassandra 9.7 Talend Open Studio 9.8 Cloudera Morphlines - Kite SDK 9.9 Impala 9.10 Mongo DB

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