BCS061 Big Data Syllabus
AKTU B.Tech · 3rd Year · Unit-wise syllabus (effective 2024-25)
BCS061 Big Data (also written as KCS061) is a subject in the 3rd year of the AKTU B.Tech programme, prescribed by Dr. A.P.J. Abdul Kalam Technical University with an L-T-P of 3-0-0. The syllabus is divided into 5 units: Introduction to Big Data, Hadoop and Map Reduce, HDFS (Hadoop Distributed File System) and Hadoop Environment, NoSQL Databases, Hadoop Eco System Frameworks. The complete unit-wise topic list, course outcomes and reference books are given below, taken from the official AKTU syllabus effective from 2024-25.
BCS061 Subject Details
| Subject Code | BCS061 / KCS061 |
|---|---|
| Subject Name | Big Data |
| University | Dr. A.P.J. Abdul Kalam Technical University (AKTU) |
| Course | B.Tech (Bachelor of Technology) |
| Year / Semester | 3rd Year |
| L-T-P | 3-0-0 |
| Total Units | 5 |
| Scheme Effective From | 2024-25 |
| Last Verified | 2026-08-17 |
BCS061 Big Data Unit-Wise Syllabus
Official AKTU syllabus (effective 2024-25) — 5 units.
Unit 1: Introduction to Big Data
Introduction to Big Data: Types of digital data, history of Big Data innovation, introduction to Big Data platform, drivers for Big Data, Big Data architecture and characteristics, 5 Vs of Big Data, Big Data technology components, Big Data importance and applications, Big Data features – security, compliance, auditing and protection, Big Data privacy and ethics, Big Data Analytics, Challenges of conventional systems, intelligent data analysis, nature of data, analytic processes and tools, analysis vs reporting, modern data analytic tools.
Unit 2: Hadoop · Map Reduce
Hadoop: History of Hadoop, Apache Hadoop, the Hadoop Distributed File System, components of Hadoop, data format, analyzing data with Hadoop, scaling out, Hadoop streaming, Hadoop pipes, Hadoop Echo System. Map Reduce: Map Reduce framework and basics, how Map Reduce works, developing a Map Reduce application, unit tests with MR unit, test data and local tests, anatomy of a Map Reduce job run, failures, job scheduling, shuffle and sort, task execution, Map Reducetypes, input formats, output formats, Map Reduce features, Real-world Map Reduce
Unit 3: HDFS (Hadoop Distributed File System) · Hadoop Environment
HDFS (Hadoop Distributed File System): Design of HDFS, HDFS concepts, benefits and challenges, file sizes, block sizes and block abstraction in HDFS, data replication, how does HDFS store, read, and write files, Java interfaces to HDFS, command line interface, Hadoop file system interfaces, data flow, data ingest with Flume and Scoop, Hadoop archives, Hadoop I/O: compression, serialization, Avro and file-based data structures. Hadoop Environment: Setting up a Hadoop cluster, cluster specification, cluster setup and installation, Hadoop configuration, security in Hadoop, administering Hadoop, HDFS monitoring & maintenance, Hadoop benchmarks, Hadoop in the cloud Hadoop Eco System and YARN: Hadoop ecosystem components, schedulers, fair and capacity, Hadoop 2.0 New Features - NameNode high availability, HDFS federation,MRv2, YARN, Running MRv1 in YARN.
Unit 4: NoSQL Databases
NoSQL Databases: Introduction to NoSQL MongoDB: Introduction, data types, creating, updating and deleing documents, querying, introduction to indexing, capped collections Spark: Installing spark, spark applications, jobs, stages and tasks, Resilient Distributed Databases, anatomy of a Spark job run, Spark on YARN SCALA: Introduction, classes and objects, basic types and operators, built-in control structures, functions and closures, inheritance.
Unit 5: Hadoop Eco System Frameworks
Hadoop Eco System Frameworks: Applications on Big Data using Pig, Hive and HBase Pig - Introduction to PIG, Execution Modes of Pig, Comparison of Pig with Databases, Grunt, Pig Latin, User Defined Functions, Data Processing operators, Hive - Apache Hive architecture and installation, Hive shell, Hive services, Hive metastore, comparison with traditional databases, HiveQL, tables, querying data and user defined functions, sorting and aggregating, Map Reduce scripts, joins & subqueries. HBase – Hbase concepts, clients, example, Hbase vs RDBMS, advanced usage, schema design, advance indexing, Zookeeper – how it helps in monitoring a cluster, how to build applications with Zookeeper. IBM Big Data strategy, introduction to Infosphere, BigInsights and Big Sheets, introduction to Big SQL.
BCS061 Course Outcomes
- At the end of course , the student will be able to
- CO 1 Demonstrate knowledge of Big Data Analytics concepts and its applications in business. K1,K2
- CO 2 Demonstrate functions and components of Map Reduce Framework and HDFS. K1,K2
- CO 3 Discuss Data Management concepts in NoSQL environment. K6
- CO 4 Explain process of developing Map Reduce based distributed processing applications. K2,K5
- CO 5 Explain process of developing applications using HBASE, Hive, Pig etc. K2,K5
BCS061 Reference Books
- Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business
- DT Editorial Services, Big-Data Black Book, Wiley
- Dirk deRoos, Chris Eaton, George Lapis, Paul Zikopoulos, Tom Deutsch, “Understanding Big Data Analytics for
- Thomas Erl, Wajid Khattak, Paul Buhler, “Big Data Fundamentals: Concepts, Drivers and Techniques”, Prentice
- Bart Baesens “Analytics in a Big Data World: The Essential Guide to Data Science and its Applications (WILEY
- ArshdeepBahga, Vijay Madisetti, “Big Data Science & Analytics: A HandsOn Approach “, VPT
- Anand Rajaraman and Jeffrey David Ullman, “Mining of Massive Datasets”, CUP
- Tom White, "Hadoop: The Definitive Guide", O'Reilly.
- Eric Sammer, "Hadoop Operations", O'Reilly.
- Chuck Lam, “Hadoop in Action”, MANNING Publishers
- Deepak Vohra, “Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools”,
- E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilly
- Lars George, "HBase: The Definitive Guide", O'Reilly.
- Alan Gates, "Programming Pig", O'Reilly.
- Michael Berthold, David J. Hand, “Intelligent Data Analysis”, Springer
- Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced
- Glenn J. Myatt, “Making Sense of Data”, John Wiley & Sons
- Pete Warden, “Big Data Glossary”, O’Reilly
Frequently Asked Questions about BCS061
What is the syllabus of BCS061 Big Data?
The AKTU BCS061 Big Data syllabus is divided into 5 units: Introduction to Big Data; Hadoop and Map Reduce; HDFS (Hadoop Distributed File System) and Hadoop Environment; NoSQL Databases; Hadoop Eco System Frameworks. The complete topic list for each unit is published on this page, taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus effective from 2024-25.
How many units are there in BCS061?
BCS061 Big Data has 5 units in the AKTU syllabus, with an L-T-P (Lecture-Tutorial-Practical) structure of 3-0-0.
Is BCS061 the same subject as KCS061?
Yes. AKTU renumbered its B.Tech subject codes from the 2022-23 session onwards, so the same subject appears as BCS061 and KCS061 depending on the scheme year. The syllabus content is the same.
Where can I download BCS061 previous year question papers?
AKTU B.Tech previous year question papers for every semester are available to download free on Ryzenstudy, with no login or registration required.
Is this the official AKTU syllabus for BCS061?
Yes. Every topic on this page is taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus document (AKTU B.Tech 3rd Year CS / Computer Engineering / CSE Syllabus), effective from 2024-25. A direct link to the original AKTU PDF is provided at the top of this page. Last verified on 2026-08-17.
Related AKTU Syllabus
Source: Dr. A.P.J. Abdul Kalam Technical University official syllabus — AKTU B.Tech 3rd Year CS / Computer Engineering / CSE Syllabus, effective from 2024-25. Ryzenstudy is not affiliated with AKTU. Content last verified on 2026-08-17.