Big Data Analytics Notes, Syllabus & Lab Manual (PDF)
Big data analytics VTU notes cover five modules Introduction to Big Data, Hadoop & HDFS, MapReduce, NoSQL Databases, and Apache Spark for the 7th-semester CBCS elective (subject codes 18CS755 / 21CS71).
This page provides unit-wise study material, the complete syllabus breakdown, a lab manual with practicals, and PDF downloads aligned to both VTU and Anna University exam patterns, including notes in Hindi.
What Do Big Data Analytics VTU Notes Cover?
- Module 1: Introduction to Big Data 5 Vs, data types, ecosystem overview
- Module 2: Hadoop & HDFS architecture, NameNode, DataNode, replication
- Module 3: MapReduce Map, Shuffle, Reduce, job execution flow
- Module 4: NoSQL Databases HBase, MongoDB, CAP theorem, column-family stores
- Module 5: Analytics & Spark RDDs, MLlib, Spark Streaming, GraphX
According to the International Data Corporation (IDC), the global datasphere is expected to reach 175 zettabytes by 2025, with India among the fastest-growing data-generating economies.
A 2023 survey by NASSCOM found that data engineering and analytics roles account for over 30% of new tech hiring in India making big data analytics VTU notes some of the most career-relevant study material in your engineering degree. Don’t treat this as just another paper to pass.
Ready to test your knowledge right away?
Our Big Data Exam Prep section has previous year VTU and Anna University question papers with model answers.
Big Data Analytics Syllabus Overview
Big Data Analytics is typically offered in the 6th or 7th semester for CSE and ISE students across Indian universities.
The subject sits at the intersection of distributed computing, storage systems, and analytical modelling and it’s become one of the most job-relevant papers in the engineering curriculum.
VTU Big Data Analytics Syllabus (18CS755 / 21CS71)
Under Visvesvaraya Technological University (VTU), Big Data Analytics is a 7th-semester elective under the CBCS scheme. The 2018 scheme uses code 18CS755; the 2021 scheme uses 21CS71 the module structure is broadly similar but the 2021 scheme integrates more Spark and cloud-native tooling.
The official VTU syllabus is published at vtu.ac.in always cross-check the current scheme year before downloading notes.
| Module | Topic | Key Concepts |
|---|---|---|
| Module 1 | Introduction to Big Data | 5 Vs, Data types, Big Data ecosystem |
| Module 2 | Hadoop & HDFS | Architecture, NameNode, DataNode, replication |
| Module 3 | MapReduce | Map, Shuffle, Reduce, job execution flow |
| Module 4 | NoSQL Databases | HBase, MongoDB, CAP theorem, column-family stores |
| Module 5 | Analytics & Spark | Apache Spark, RDDs, MLlib, streaming analytics |
Each module carries roughly equal weightage in the 100-mark theory paper, split between CIE (50 marks) and SEE (50 marks). The VTU question paper pattern typically asks students to answer five questions, one from each module.
Anna University Big Data Analytics Syllabus (CS8091)
Anna University offers Big Data Analytics under course code CS8091 (R-2017 regulation) for B.E. CSE students in the 8th semester. The official regulation document is available at annauniv.edu.
Anna University’s version places heavier emphasis on analytics techniques and statistical tools including R and Python compared to VTU, which leans more towards infrastructure and distributed storage.
The five units are: Introduction, Hadoop Ecosystem, MapReduce Design, NoSQL, and Data Analytics with R/Python.
Key Takeaway
Both syllabi converge on Hadoop, HDFS, MapReduce, and NoSQL so core concepts transfer between universities. VTU goes deeper on distributed storage architecture; Anna University integrates more analytics tooling. Our big data analytics VTU notes are structured to serve both patterns.
Unit-Wise Big Data Analytics Notes
Unit-wise notes are the most practical study format for semester exams. Rather than reading textbooks cover-to-cover, you need targeted, exam-pattern-aligned material.
Here’s what each major unit covers and what you must know cold for your big data analytics VTU notes revision.
HDFS & Hadoop Architecture
Hadoop is an open-source Apache framework that enables distributed storage and processing of large datasets across clusters of commodity hardware.
It’s the foundation of the entire big data ecosystem and almost certainly appears in your first or second module.
HDFS (Hadoop Distributed File System) splits files into 128 MB blocks by default and replicates each block three times across different DataNodes. The NameNode manages metadata; DataNodes store actual data.
This architecture tolerates hardware failure without data loss that’s the design insight examiners love to test.
- NameNode: Master node; stores file system namespace and metadata
- DataNode: Slave nodes; store actual data blocks
- Secondary NameNode: Checkpointing helper not a backup NameNode (a common exam trap)
- Replication factor: Default 3; configurable per file
- Block size: 128 MB (Hadoop 2.x+); was 64 MB in Hadoop 1.x
For VTU students specifically, questions on HDFS architecture with a neat diagram fetch full marks.
Practice drawing the read and write pipelines they appear in almost every question paper from 2019 onwards.
MapReduce Programming Model
MapReduce is a programming model for processing large datasets in parallel across a distributed cluster. It breaks computation into two phases: the Map phase (processes input key-value pairs and produces intermediate pairs) and the Reduce phase (merges intermediate values sharing the same key).
Between Map and Reduce sits the Shuffle and Sort phase the most misunderstood part. It groups all values with the same key and sends them to the same Reducer.
Understanding this phase separates students who score 8/10 from those who score 5/10.
- InputFormat: Defines how input files are split and read
- Mapper: Processes each input record independently
- Combiner: Mini-reducer that runs locally to reduce network traffic
- Partitioner: Determines which Reducer receives which key
- Reducer: Aggregates values for each key into final output
A classic exam question: implement a word count program using MapReduce.
Know the pseudocode, the data flow, and what happens when a node fails mid-job the ApplicationMaster reschedules the failed task on another node.
Apache Spark & Analytics Techniques
This unit covers the analytics half of the subject. Topics include classification, clustering, regression, association rule mining, and recommendation systems applied to large datasets using Apache Spark, R, and Python.
Apache Spark is up to 100x faster than Hadoop MapReduce for in-memory processing, according to benchmarks published at spark.apache.org.
It introduced RDDs (Resilient Distributed Datasets) fault-tolerant, immutable distributed collections that can be processed in parallel.
Spark’s ecosystem includes MLlib for machine learning, Spark Streaming for real-time data, GraphX for graph processing, and Spark SQL for structured queries.
For exam purposes, focus on RDD operations (transformations vs. actions) and Spark architecture (Driver, Executors, Cluster Manager).
For deeper concept explanations covering HDFS internals, Spark architecture, and NoSQL data models, see our Big Data Concepts guide.
Big Data Analytics Lab Manual for VTU 7th Semester
The lab component is where most students lose easy marks by not practising enough. The big data analytics lab manual for VTU typically includes 10–12 experiments covering Hadoop installation, HDFS operations, MapReduce programs, Hive queries, HBase operations, and Spark programs.
According to the All India Council for Technical Education (AICTE) model curriculum, practical hours for this subject are typically 2 hours per week across 15 weeks 30 lab sessions you shouldn’t be cramming the night before your practical exam.
Core Lab Experiments You Must Know
- Hadoop Single-Node Setup: Install Hadoop on Ubuntu, configure core-site.xml, hdfs-site.xml, and mapred-site.xml. Start NameNode and DataNode services.
- HDFS Commands: Practice
hdfs dfs -mkdir,-put,-get,-ls,-cat, and-rm. These appear in viva questions every single year. - Word Count in MapReduce: Write, compile, and run the canonical word count program. Understand JAR packaging and job submission.
- Hive Queries: Create tables, load data, run HiveQL SELECT, GROUP BY, and JOIN queries. Compare with SQL to understand the abstraction layer.
- HBase Operations: Create a table, insert rows using
put, retrieve usinggetandscan. Understand the column-family model. - Spark RDD Operations: Create RDDs from text files, apply
map(),filter(),reduceByKey(), andcollect()actions. - Spark MLlib: Implement a basic classification or clustering algorithm (k-means is common) on a sample dataset.
For VTU lab exams, you’ll be given a problem statement 30 minutes before execution. If you’ve practised each experiment at least three times, you won’t panic.
If you haven’t you will.
Big Data Analytics Notes PDF Download
Downloadable PDFs are the format most engineering students actually study from — especially during revision week.
On 3.0 University.io, you can access big data analytics notes PDF files organised unit-wise for both VTU (18CS755 / 21CS71) and Anna University (CS8091) patterns. No login walls, no broken links.
Our big data analytics VTU notes are written originally by subject matter experts with teaching experience in Indian engineering colleges.
They’re not scanned textbook copies they’re structured, exam-pattern-aligned study material with solved examples, important questions, and model answers.
Big Data Analytics Notes in Hindi (हिंदी में नोट्स)
Yes, Hindi-medium notes are available. A significant portion of our readers particularly students from Uttar Pradesh, Madhya Pradesh, Rajasthan, and Bihar are more comfortable reading technical explanations in Hindi.
Our Hindi notes cover core concepts without sacrificing technical accuracy.
Hindi-language big data analytics notes cover: बड़े डेटा का परिचय (Introduction to Big Data), HDFS की संरचना (HDFS Architecture), MapReduce की कार्यप्रणाली (MapReduce working), and NoSQL डेटाबेस (NoSQL Databases). These are available as separate PDF downloads on the notes page.
Recommended Reference Books for Big Data Analytics VTU Notes Preparation
| Book Title | Author(s) | Relevance |
|---|---|---|
| Big Data: A Revolution That Will Transform How We Live, Work, and Think | Viktor Mayer-Schönberger & Kenneth Cukier | Conceptual foundation, Module 1 |
| Hadoop: The Definitive Guide | Tom White (O’Reilly) | HDFS & MapReduce deep dive |
| Learning Spark | Jules Damji et al. (O’Reilly) | Spark RDDs, DataFrames, MLlib |
| NoSQL Distilled | Martin Fowler & Pramod Sadalage | CAP theorem, HBase, MongoDB |
For a curated reading list with Indian pricing and availability, see our Big Data Books page.
Frequently Asked Questions
Where can I get big data analytics VTU notes?
You can access big data analytics VTU notes right here on 3University.io, organised unit-wise for the VTU CBCS scheme (18CS755 / 21CS71). The notes cover all five modules from HDFS and MapReduce through to Spark and NoSQL — in an exam-pattern-aligned format. PDF downloads are available without registration.
What is the big data analytics VTU syllabus?
The VTU syllabus covers five modules: Introduction to Big Data, Hadoop & HDFS, MapReduce, NoSQL Databases, and Apache Spark analytics. The 2018 scheme (18CS755) and 2021 scheme (21CS71) follow the same broad structure, with the 2021 scheme adding more Spark and cloud-native content. Always verify the exact module list against your university’s official scheme document.
Where can I download big data analytics notes PDF?
Unit-wise big data analytics notes PDF files are available on this page and linked from the 3University.io notes library. These are original, exam-ready documents not scanned textbook pages. They include important questions, definitions, diagrams, and model answers aligned to VTU and Anna University question paper patterns.
Is there a big data analytics lab manual for VTU?
Yes. The big data analytics lab manual on 3University.io covers 10+ experiments including Hadoop single-node setup, HDFS command-line operations, MapReduce word count, Hive queries, HBase CRUD operations, and Spark RDD programs. Each experiment includes step-by-step instructions, expected output, and common viva questions.
Are big data analytics notes available in Hindi?
Yes, Hindi-medium notes are available covering core topics like HDFS architecture, MapReduce working, and NoSQL databases in clear Hindi explanations. These are particularly useful for students from Hindi-speaking states who find English-only technical material harder to absorb quickly during revision.
Download links are on the main notes page.
Which is the best book for big data analytics VTU exam preparation?
Hadoop: The Definitive Guide by Tom White is the most comprehensive reference for HDFS and MapReduce modules. For Spark, Learning Spark by Jules Damji et al. is the standard. For conceptual clarity on Module 1, Viktor Mayer-Schönberger’s Big Data is accessible and exam-relevant.
What is the difference between VTU 2018 scheme and 2021 scheme for big data analytics?
The 2018 scheme (18CS755) focuses more heavily on Hadoop infrastructure, HDFS internals, and MapReduce programming. The 2021 scheme (21CS71) retains these core modules but integrates more Apache Spark content, cloud-native data processing concepts, and updated analytics tooling. Always download notes labelled for your specific scheme year.
Download the Big Data Analytics Notes, Syllabus & Lab Manual (PDF)
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