Department Events
BIG DATA TECHNOLOGIES
Course Objectives
This Course enables the students to
- Define the basic concepts of big data.
- Understand the concepts of big data technologies.
- Introduce the tools required to manage and analyze big data
- Relate data management by RDBMS & NOSQL.
- Generate applications using map reduce.
- Develop skills to solve complex real world problems.
Course Outcomes(COs):
Learning Outcome (at course level)
| Learning and teaching strategies | Assessment Strategies |
| Approach in teaching: Interactive Lectures, Tutorials, Demonstrations, Flipped classes.
Learning activities for the students: Self-learning assignments, Quizzes, Presentations, Discussions
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Understanding Big Data
Introduction, Need, convergence of key trends, structured data Vs. unstructured data , industry examples of big data, web analytics – big data and marketing, fraud and big data, risk and big data, credit risk management, big data and algorithmic trading, big data and its applications in healthcare, medicine, advertising etc.
Big Data Technologies: Hadoop
Open source technologies, cloud and big data, Crowd Sourcing Analytics, inter and trans firewall analytics
Introduction to Hadoop, Data format, analyzing data with Hadoop, scaling out, Hadoop streaming, Hadoop pipes. Design of Hadoop distributed file system (HDFS), HDFS concepts – Java interface, data flow, Data Ingest with Flume and Sqoop. Hadoop I/O – data integrity, compression, serialization, Avro – file-based data structures.
Hadoop Related Tools:
Introduction to Hbase: The Dawn of Big Data, the Problem with Relational Database Systems. Introduction to Cassandra: Introduction to Pig, Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation – HiveQL queries.
NOSQL Data Management:
Introduction to NoSQL, aggregate data models, key-value and document data models, relationships, graph databases, schemaless databases, materialized views, distribution models, sharding, master-slave replication, peer-peer replication Consistency: relaxing consistency, version stamps
Map Reduce Applications:
MapReduce workflows, unit tests with MRUnit, test data and local tests, anatomy of MapReduce job run, classic Map-reduce – YARN, failures in classic Map-reduce and YARN – job scheduling, shuffle and sort, task execution, MapReduce types – input formats – output formats, MapReduce – partitioning and combining, Composing MapReduce Calculations.
- Big Data, Black Book, DT Editorial Services, DreamTech Press 2016.
- Professional NOSQL, Shashank Tiwari, Wrox, September 2011.
- Big Data and Analytics, 2ed, Subhashini Chellappan, Seema Acharya, Wiley, 2019.
Suggested Readings:
- HBase: The Definitive Guide, 2e, Lars George, O'Reilley, 2014.
- Programming Pig, Alan Gates, O'Reilley, 2017.
- NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, P. J. Sadalage and M. Fowler, Pearson Education, Inc. 2012.
- Programming Hive, 2e, E. Capriolo, D. Wampler, and J. Rutherglen, O'Reilley, 2017.
E-Resources:
- NPTEL MOOC Course on Big Data Computing, Prof. Rajiv Misra, Department of Computer science and Engineering, IIT Patna, https://nptel.ac.in/courses/106104189.
- MapReduce: Simplified Data Processing on Large Clusters, Jeffrey Dean and Sanjay Ghemawat jeff@google.com, sanjay@google.com Google, Inc., http://static.googleusercontent.com/media/research.google.com/en/us/archive/mapreduce-osdi04.pdf
- Map Reduce (hands on), Zbigniew Baranowski, https://cds.cern.ch/record/2041327; CERN Document Server, NDLI
- Big Data - Tim Smith, TEDEd Animation, https://ed.ted.com/lessons/exploration-on-the-big-data-frontier-tim-smith.
- Introduction to Hadoop, Matthias Braeger, CERN school on Grid and Advanced Information Systems, NDLI, https://edms.cern.ch/document/1283414/1
