Big Data Engineering and Analytical Methods
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Big Data Engineering and Analytical Methods Syllabus — Unit-wise Content
The official GTU curriculum for Big Data Engineering and Analytical Methods (subject code 3174807) as prescribed for CS-Cyber · Semester 7 divides the course into 7 units, covering 53 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3174807)Unit 1 Introduction to Big Data
- Introduction to Big Data
- Big Data characteristics
- Challenges of Conventional System
- Types of Big Data
- Intelligent data analysis
- Traditional vs
- Big Data business approach
- Case Study of Big Data Solutions
Unit 2 Hadoop
- Introduction to Hadoop and HDFS
- including node architecture and key components
- Overview of data processing using MapReduce
- its working
- job execution flow
- error handling, and scheduling
- Discussion on HDFS design
- Hadoop Streaming
- Java HDFS APIs, and cluster setup
- Basics of cluster configuration
- administration, and Hadoop’s use in cloud environments and performance benchmarking
Unit 3 NoSQL
- NoSQL refers to non-relational databases designed to handle large volumes of diverse and fast-changing data
- It emerged due to business needs for scalable
- flexible data storage
- Common types include key-value
- column-family, and document stores
- NoSQL systems use varied architectures to manage big data
- often relying on shared-nothing models
- They solve challenges using distribution methods like master-slave and peer- to-peer
- each optimized for different scalability and performance needs
Unit 4 Mining Data Stream
- Introduction to Streams Concepts
- Stream Data Model and Architecture
- Stream Computing
- Sampling Data in a Stream
- Filtering Streams
- Counting Distinct Elements in a Stream
- Estimating moments
- Counting oneness in a Window
- Decaying Window
- Real time Analytics Platform (RTAP) applications
- Case Studies
- Real Time Sentiment Analysis
- Stock Market Predictions
- Using Graph Analytics for Big Data: Graph Analytics
Unit 5 Frameworks
- Applications on Big Data Using Pig and Hive
- Data processing operators in Pig, Hive services
- Querying Data in Hive
- fundamentals of HBase and ZooKeeper
- IBM InfoSphere BigInsights and Streams
Unit 6 Spark
- Introduction to Data Analysis with Spark
- In-Memory Computing with Spark
- Spark Basics
- Interactive Spark with PySpark
- Writing Spark Applications
Unit 7 Program Name
- Bachelor of Engineering Level: UG Subject Code : 3174807 Subject Name :Big Data Engineering and Analytical Methods w.e.f. 2025-26 http://syllabus.gtu.ac.in/ Page no. 1 of
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