BCDS601 Big Data and Analytics Syllabus
AKTU B.Tech · 6th Semester · Unit-wise syllabus (effective 2024-25)
BCDS601 Big Data and Analytics (also written as KCDS601) is a subject in Semester 6 of the AKTU B.Tech programme, prescribed by Dr. A.P.J. Abdul Kalam Technical University. The syllabus is divided into 5 units: Introduction to Big Data, Hadoop and Map Reduce, HDFS (Hadoop Distributed File System) and Hadoop Environment, Hadoop Eco System and YARN and 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.
BCDS601 Subject Details
| Subject Code | BCDS601 / KCDS601 |
|---|---|
| Subject Name | Big Data and Analytics |
| University | Dr. A.P.J. Abdul Kalam Technical University (AKTU) |
| Course | B.Tech (Bachelor of Technology) |
| Year / Semester | 3rd Year · 6th Semester |
| Total Units | 5 |
| Scheme Effective From | 2024-25 |
| Last Verified | 2026-08-19 |
BCDS601 Big Data and Analytics 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 Reduce types, 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
Unit 4: Hadoop Eco System and YARN · NoSQL Databases
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. 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 userdefined 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.
BCDS601 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
- 3-0-0
BCDS601 Reference Books
- Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business
- Big-Data Black Book, DT Editorial Services, 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
- Raj Kamal, Preeti Saxena, “Big Data Analytics”, McGraw Hill Education
- 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
- Anil Maheshwari, “Big Data”, Second Edition, McGraw Hill
- 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 BCDS601
What is the syllabus of BCDS601 Big Data and Analytics?
The AKTU BCDS601 Big Data and Analytics syllabus is divided into 5 units: Introduction to Big Data; Hadoop and Map Reduce; HDFS (Hadoop Distributed File System) and Hadoop Environment; Hadoop Eco System and YARN and 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 BCDS601?
BCDS601 Big Data and Analytics has 5 units in the AKTU syllabus.
Is BCDS601 the same subject as KCDS601?
Yes. AKTU renumbered its B.Tech subject codes from the 2022-23 session onwards, so the same subject appears as BCDS601 and KCDS601 depending on the scheme year. The syllabus content is the same.
Which semester is BCDS601 taught in?
BCDS601 Big Data and Analytics is taught in Semester 6 of the 3rd year of the AKTU B.Tech programme.
Where can I download BCDS601 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 BCDS601?
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 Computer Science and Engineering (Data Science) Syllabus), effective from 2024-25. A direct link to the original AKTU PDF is provided at the top of this page. Last verified on 2026-08-19.
Related AKTU Syllabus
Source: Dr. A.P.J. Abdul Kalam Technical University official syllabus — AKTU B.Tech 3rd Year Computer Science and Engineering (Data Science) Syllabus, effective from 2024-25. Ryzenstudy is not affiliated with AKTU. Content last verified on 2026-08-19.