Subject Syllabus Parul’ scaset-tlgoma ate University Btech Prerequisite: Database Management system, SQL Course Objective: Big data analytics is the often complex pracess of examining big data to uncover information such as hidden patterns, correlations, market trends and customer preferences that can help organizations make informed business decisions Teaching and Examination Scheme Teaching Scheme Examination Scheme Lecture | Tutorial | tab Internal Marks External Marks Total Hrs/Week |Hrs/Week Hrs/Week Hrs/Week “esl ce ? 7 P 3 ° ° 0 3 2 | 2 - 60 100 ‘SEE - Somestor End Examination, T- Theory, P- Practical (Course Content W- Weightage (0), T- Teaching hours ‘sr. |Topies wit 1 Introduction 20) 9 What Is in Store? , Classification of Digital Data: Structured, Semi Structured & Un Structured , Evolution of Big Data, Definition of Big Data - Volume - Velocity 4Variety, Challenges of Big Data , Why Big Data?, Traditional Business Intelligence (8!) versus Big Data, industry examples of big data , What is Big Data Analytics? Data Science 2 | Nosql Data Management: 20) 9 Introduction to NoSQL, Types of NoSQl., Why NoSQL?, Advantages of NoSAlL, Comparison of SQL, NoSQL and NewSQL, aggregates , key-value and document data models, graph databases, map-reduce, partitioning and combining '3 | Basics Of Hadoop: 40 | 18 What is Hadoop?, Brief History of Hadoop , Why Hadoop? , RDBMS versus Hadoop , Hadoop Components, High Level Architecture of Hadoop , Key Advantages & Features of Hadoop , Data format Hadoop distributed file system (HDFS) , Processing Data with Hadoop. Map Reduce Interface:Overview of Map Reduce, Map-Reduce workflows, anatomy of Map-Reduce job run, shuffle and sort task execution input formats , output formats. [4 [Hadoop Related Tools: 20] 9 loverview of HBase, Pig introduction, Pig data model, Hive, data types and file formats, HiveQl data definition, HiveQt data manipulation, HiveQl. queries, Pig Latin Overview , Pig versus Hive, Using JSON , Overview of Cassandra, Lasper Reports [Reference Books 1. |Hadoop: The Definitive Guide by Tom White, Third Edition, O'Reilley. (TextBook) By Tom White 2. [Understanding Big data By Chris Eaton, Dirk derooset a. | McGraw Hill, Pub. Year 2012 3. |Hadoop Operations By Eric Sammer | O'Reilley, Pub. Year 2012 “4. Big data analytics with R and Hadoop, VigneshPrajapati SPD. By VigneshPrajapati 5. [Big Data and Analytics By Seema Acharya and Subhashini C | Wiley India 6. | Programming Hive, E. Capriolo, D. Wampler, and J. Rutherglen, O'Reilley By E. Capriolo, D. Wampler, and J. Rutherglen 7. |MongoDB in Action By Kyle Banker, Piter Bakkumn Shaun Verch | Dream tech Press ‘8 _ | HBase: The Definitive Guide, Lars George, O'ReilleyParul’ Subject Syllabus SBE university [Course Outcome ‘After Learning the Course the students shall be able 2. Understand the Big Data flow 2. Solve problems using MapReduce technique 13. Implement single-node/multimode Hadoop cluster 4. Differentiate between conventional SAL query language and NoSQL 5. Apply the various technologies and tools associated with Big Data such as HDFS, Map Reduce, Pig, Hive, MongoD8{ ie » Parul sss agone most wou, University BTech > Course Prerequisite: Database Management system, SQL Course Objective: Big data analytics is the often complex process of examining big data to uncover information ~such as hidden patterns, correlations, market trends and customer preferences ~ that can help organizations make informed business decisions [Teaching and Examination Scheme Teaching Scheme Examination Scheme Lecture | Tutorial Lab Internal Marks External Marks Total Hirs/Week | Hrs/Week Hrs/Week Hrs/Week “Tea T cE > T P 0 0 2 0 1 = 20 - 30 50 ‘SEE - Somostor End Examination, T- Toor, P- Practical (Course Outcome [After Learning the Course the students shall be able tor 2. Understand the Big Data flow 12. Solve problems using the MapReduce technique 13. Implement single-node/multimade Hadoop cluster 4. Differentiate between conventional SAL query language and NoSQL [5 Apply the various technologies and tools associated with Big Data such as HDFS, Map Reduce, Pig, Hive, MongoD8 List of Practical 1.__|[Tounderstand the overall programming architecture using Map Reduce API. 2. __ [Write a program of Word Count in Map Reduce over HOFS. 3. _ [Basic CRUD operations in MongoD8, ‘Store the basic information about students such as roll no, name, date of birth, and address of student using various * collection types such as List, Set and Map. 5.__|Basic commands available for the Hadoop Distributed File System, [Basic commands available for HIVE Query Language. 77__|Basic commands of HBASE Shell [s.__ [creating the HDFS tables and loading them in Hive and learn joining of tables in Hive.