Big Data Analytics
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Big Data Analytics Syllabus — Unit-wise Content
The official GTU curriculum for Big Data Analytics (subject code 3174207) as prescribed for AI & DS · Semester 7 divides the course into 6 units, covering 86 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 (3174207)Unit 1 Introduction to Big Data
- Introduction to Data
- Types of Data
- Structured Data
- Unstructured Data
- Semi- Structured Data
- Meta Data
- The Emergence of ‘New Data’
- Comparison of New and Traditional Data Data Analytics
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Perceptive Analytics
- analysis of problem space and data needs Introduction to Big Data
- Why and Where
- Big Data characteristics
- Types of Big Data
- Transition to Big Data
- Definition of Big Data
- Sources of Big Data Challenges of Conventional System
- Intelligent data analysis
- Traditional vs
- Big Data business approach
- Common Application and Case Study of Big Data Solutions
- Getting values out of Big Data
Unit 2 Hadoop
- History of Hadoop
- Hadoop Distributed File System: Physical organization of Compute Nodes
- Components of Hadoop
- Analyzing the Data with Hadoop
- Scaling Out, Hadoop Streaming
- Design of HDFS
- Java interfaces to HDFS Basics
- Developing a Map Reduce Application
- How Map Reduce Works
- Anatomy of a Map Reduce Job run, Failures
- Job Scheduling
- 2021-22 Professional Elective WEF Academic Year : Semester : Category of the Course : Subject Code: 3174207 Subject Name: Big Data Analytics https://syllabus.gtu.ac.in/Page 2 Shuffle and Sort
- Task execution
- Map Reduce Types and Formats
- Map Reduce Features
- Hadoop environment
- Hadoop Hybrids
- Setting up a Hadoop Cluster
- Cluster specification
- Cluster Setup and Installation
- Hadoop Configuration
- Security in Hadoop
- Administering Hadoop
Unit 3 NoSQL
- What is NoSQL? NoSQL business drivers
- NoSQL Databases in the Light of CAP Theorem
- NoSQL case studies
- NoSQL data architecture patterns: Key-value stores
- Graph stores
- Column family (Bigtable) stores
- Document stores
- NoSQL Database: Cassandra
- Variations of NoSQL architectural patterns
- Using NoSQL to manage big data: What is a big data NoSQL solution? Understanding the types of big data problems
- Analyzing big data with a shared-nothing architecture
- Choosing distribution models: master- slave versus peer-to-peer
- Four ways that NoSQL systems handle big data problems
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
Unit 5 Frameworks
- Applications on Big Data Using Hive and Pig, Hive Architecture
- Data Flow and Data Types in Hive
- Different Types of Tables in Hive
- Partitioning and Bucketing in Hive
- Why Apache Pig, Features of Apache Pig, Pig vs Mapreduce
- Pig Architecture
- 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
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