Big Data Systems and Analytics
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Big Data Systems and Analytics Syllabus — Unit-wise Content
The official GTU curriculum for Big Data Systems and Analytics (subject code 1729502) as prescribed for AIAD · Semester 2 divides the course into 6 units, covering 36 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 (1729502)Unit 1 Introduction to Big Data and Hadoop
- Introduction– distributed file system–Big Data and its importance
- Four Vs, Drivers for Big data
- Big data analytics
- Big data applications
- Algorithms using map reduce
- Big Data – Apache Hadoop & Hadoop Eco System
- Moving Data in and out of Hadoop – Understanding inputs and outputs of MapReduce –Data Serialization
Unit 2 HDFS, HIVE AND HIVEQL, HBASE
- Installation and Shell
- Java API
- Hive Architecture and Installation
- Comparison with Traditional Database
- HiveQL Querying Data
- Sorting And Aggregating
- Joins & Sub queries
- HBase concepts
- Advanced Usage
- Schema Design
- Advance Indexing, PIG, Zookeeper
- how it helps in monitoring a cluster Subject Name : Big Data Systems and Analytics Subject Code : 1729502
Unit 3 Data Analysis with Spark Shell
- Writing Spark Application - Spark Programming in Scala
- Python, R, Java - Application Execution
Unit 4 NoSQL
- What is it?Where It is Used Types of NoSQL databases
- Why NoSQL? Advantages of NoSQL
- Use of NoSQL in Industry
- SQL vs NoSQL
- Database for the Modern Web
Unit 5 MongoDB, Spark SQL and GraphX
- Introduction to MongoDB key features
- Core Server tools
- MongoDB through the JavaScript’s Shell
- Creating and Querying through Indexes
- principles of schema design
- Constructing queries on Databases
- collections and Documents
- MongoDB Query Language
- SQL Context – Importing and Saving data – Data frames – using SQL – GraphX overview – Creating Graph – Graph Algorithms
Unit 6 Current Trends
- The latest technology and algorithms in data analytics for real time applications
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