Andhra Pradesh State Council of Higher Education B.Sc. Computer Science/Information Technology (IT) Syllabus CBCS w.e.f.2015-2016 (Modified in April 2016) Structure of Computer Science/Information Technology (IT) Syllabus III YEAR VI SEMESTER (Cluster 1) Paper-VIII : Elective –A-2 BIG DATA TECHNOLOGY Course Objective The Objective of this course is to provide practical foundation level training that enables immediate and effective participation in big data projects. The course provides grounding in basic and advanced methods to big data technology and tools, including MapReduce and Hadoop and its ecosystem. Course Outcome Learn tips and tricks for Big Data use cases and solutions. to build and maintain reliable, scalable, distributed systems with Apache Hadoop. Able to apply Hadoop ecosystem components. UNIT I distributed file system Big Data and INTRODUCTION TO BIG DATA:Introduction – – its importance, Four V’s in bigdata, Drivers for Big data, Big data analytics, Big data applications. Algorithms map reduce, Matrix-Vector Multiplication by Map Reduce. UNIT II INTRODUCTION HADOOP : Big Data Apache Hadoop & Hadoop EcoSystem – – Moving Data in and out of Hadoop Understanding inputs and outputs of MapReduce - – Data Serialization. UNIT- III HADOOP ARCHITECTURE: Hadoop Architecture, Hadoop Storage: HDFS, Common Hadoop Shell commands , Anatomy of File Write and Read., NameNode, Secondary NameNode, and DataNode, Hadoop MapReduce paradigm, Map and Reduce tasks, Job, Tasktrackers - Cluster Setup SSH & Hadoop Configuration HDFS Administering – – – Monitoring & Maintenance. Page 34 of 50
Andhra Pradesh State Council of Higher Education B.Sc. Computer Science/Information Technology (IT) Syllabus CBCS w.e.f.2015-2016 (Modified in April 2016) Structure of Computer Science/Information Technology (IT) Syllabus III YEAR VI SEMESTER (Cluster 1) Paper-VIII : Elective –A-2 BIG DATA TECHNOLOGY Course Objective The Objective of this course is to provide practical foundation level training that enables immediate and effective participation in big data projects. The course provides grounding in basic and advanced methods to big data technology and tools, including MapReduce and Hadoop and its ecosystem. Course Outcome Learn tips and tricks for Big Data use cases and solutions. to build and maintain reliable, scalable, distributed systems with Apache Hadoop. Able to apply Hadoop ecosystem components. UNIT I INTRODUCTION TO BIG DATA:Introduction – distributed file system – Big Data and its importance, Four V’s in bigdata, Drivers for Big data, Big data analytics, Big data applications. Algorithms map reduce, Matrix-Vector Multiplication by Map Reduce. UNIT II INTRODUCTION HADOOP : Big Data – Apache Hadoop & Hadoop EcoSystem – Moving Data in and out of Hadoop – Understanding inputs and outputs of MapReduce - Data Serialization. UNIT- III HADOOP ARCHITECTURE: Hadoop Architecture, Hadoop Storage: HDFS, Common Hadoop Shell commands , Anatomy of File Write and Read., NameNode, Secondary NameNode, and DataNode, Hadoop MapReduce paradigm, Map and Reduce tasks, Job, Tasktrackers - Cluster Setup – SSH & Hadoop Configuration – HDFS Administering – Monitoring & Maintenance. Page 34 of 50
BSc-CS-IT-Syllabus-04112017.docx