Illustrate the role of mapreduce programming in various scenarios Unit I -Introduction: Types of Digital Data-Classification of Digital Data- Introduction to Big dataCharacteristics of Data- Evolution of Big data- Definition of Big data- Challenges with big data- What is big data- Why big data- data warehouse environment – hadoop environment. Unit II -Big data Analytics and The Big Data Technology Landscape: What is Big data AnalyticsClassification of Analytics- Top Challenges facing big data- Why is big data analytics important – Data Science- Terminologies used in big data environment - BASE- NoSQL- Hadoop. Unit III -Hadoop: Introducing Hadoop- Why Hadoop- Why not RDBMS- RDBMS versus HadoopDistributed Computing Challenges- History of Hadoop – Hadoop Overview – Use case of HadoopHadoop Distributors – HDFS- Processing Data with Hadoop- Mapping Resources and Applications with Hadoop YARN – Interaction with Hadoop Ecosystem. Unit IV - MongoDB and Mapreduce Programming: What is MongoDB – Why MongoDB –Terms used in RDBMS and MongoDB – Data Types in MongoDB – MongoDB Query Language. Mapreduce Introduction –Mapper – Reducer – Combiner –Partitioner – Searching – Sorting – Compression. Unit V - Introduction to Hive and Machine Language: What is Hive- Hive Architecture – Hive Data Types- Hive File Format – Hive Querey Language – RC File Implementation – SerDe- User Defined Function – Introduction to Machine Learning – Machine Learning Algorithms. Text Book: 1. Seema Acharya, Subhashini Chellappan, “Big data and Analytics”, First Edition, Wiley, 2016, ISBN: 978-8126554782. Reference Books: 1. Anil Maheshwari, “Big Data Made Accessible”, McGrawHill Education Publication, 2017, ISBN: 978-9352604548. 2. Nathan Marz, James Warren, “Big data: Principles and Best Practices of Scalable Realtime Data Systems”, Manning Publications, 2015, ISBN: 978-1617290343. 3. Sandeep Karanth, “Mastering Hadoop”, First Edition, Packt Publishing, 2014, ISBN: 9781783983643. 18CA2002 INTERNET OF THINGS Credits: 3:0:0 Course Objective: To understand the fundamental principles of Internet of Things To understand about wireless sensor networks To develop IoT based applications Course Outcome: Students will be able to Understand the fundamental concepts of Internet of Things Understand how to interconnect software and hardware Analyze protocols in wireless sensor networks Design IoT applications in various domains Evaluate performance of IoT applications Develop sensor based applications through embedded platform Unit I - Introduction to IoT: Defining IoT, Characteristics of IoT, Physical Design of IoT, Logical design of IoT, Functional blocks of IoT, Communication models & APIs. IoT& M2M:Machine to Machine, Difference between IoT and M2M, Software define Network. Unit II - Network & Communication aspects: Wireless medium access issues, MAC protocol survey, Survey routing protocols, Sensor deployment & Node discovery, Data aggregation & Dissemination. Unit III - Challenges in IoT: Design challenges, Development challenges, Security challenges, Other challenges. Unit IV - Domain specific applications of IoT: Home automation, Industry applications, Surveillance applications, Other IoT applications.
INFORMATION TECHNOLOGY
