BIG DATA ANALYTICS 3170722 Syllabus Download With Weightage

BIG DATA ANALYTICS 3170722 Syllabus Download With Weightage

BIG DATA ANALYTICS 3170722 is presented in the 7th semester of the Computer department.

Sr. No.

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.



History of Hadoop, Hadoop Distributed File System: Physical organization of
Compte 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, Shuffle and Sort, Task execution, Map Reduce Types and
Formats, Map Reduce Features, Hadoop environment. Setting up a Hadoop Cluster,
Cluster specification, Cluster Setup and Installation, Hadoop Configuration, ecurity in
Hadoop, Administering Hadoop, Monitoring-Maintenance, Hadoop benchmarks, Hadoop
in the cloud



What is NoSQL? NoSQL business drivers; NoSQL case studies; NoSQL data
architecture patterns: Key-value stores, Graph stores, Column family (Bigtable) stores,
Document stores, 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


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



Applications on Big Data Using Pig and Hive, Data processing operators
in Pig, Hive services, HiveQL, Querying Data in Hive, fundamentals of HBase and
ZooKeeper, IBM InfoSphere BigInsights and Streams.



Introduction to Data Analysis with Spark, In-Memory Computing with Spark,
Spark Basics, Interactive Spark with PySpark, Writing Spark Applications



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