Subject code CCS334 deals with semester V of B.Tech Artificial Intelligence and Data Science regarding affiliated institutions of Anna University Regulation 2021 Syllabus. In this article, you can gather certain information relevant to the Big Data Analytics. We added the information by expertise.
We included the proper textbooks and references to assist in some way in your preparation. It will enhance your preparation and strategies to compete with the appropriate spirit with others in the examination. If you see, you can find the detailed syllabus of this subject unit-wise without leaving any topics from the unit. In this article CCS334 – Big Data Analytics Syllabus, You can simply read the following syllabus. Hope you prepare well for the examinations. I hope this information is useful. Don’t forget to share with your friends.
If you want to know more about the syllabus of B.Tech Artificial Intelligence And Data Science connected to an affiliated institution’s four-year undergraduate degree program. We provide you with a detailed Year-wise, semester-wise, and Subject-wise syllabus in the following link B.Tech Artificial Intelligence And Data Science Syllabus Anna University, Regulation 2021.
Aim of Objectives:
- To understand big data.
- To learn and use NoSQL big data management.
- To learn MapReduce analytics using Hadoop and related tools.
- To work with map-reduce applications.
- To understand the usage of Hadoop-related tools for Big Data Analytics.
CCS334 – Big Data Analytics Syllabus
Unit I: Understanding Big Data
Introduction to big data – convergence of key trends – unstructured data – industry examples of big data – web analytics – big data applications– big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics.
Unit II: Nosql Data Management
Introduction to NoSQL – aggregate data models – key-value and document data models – relationships – graph databases – schemaless databases – materialized views – distribution models – master-slave replication – consistency – Cassandra – Cassandra data model – Cassandra examples – Cassandra clients.
Unit III: Basics Of Hadoop
Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures Cassandra – Hadoop integration.
Unit IV: Map Reduce Applications
MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN – job scheduling – shuffle and sort – task execution – MapReduce types – input formats – output formats.
Unit V: Hadoop Related Tools
Hbase – data model and implementations – Hbase clients – Hbase examples – praxis. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation – HiveQL queries.
Text Book:
- Michael Minelli, Michelle Chambers, and AmbigaDhiraj, “Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses”, Wiley, 2013.
- Eric Sammer, “Hadoop Operations”, O’Reilley, 2012.
- Sadalage, Pramod J. “NoSQL distilled”, 2013
References:
- E. Capriolo, D. Wampler, and J. Rutherglen, “Programming Hive”, O’Reilley, 2012.
- Lars George, “HBase: The Definitive Guide”, O’Reilley, 2011.
- Eben Hewitt, “Cassandra: The Definitive Guide”, O’Reilley, 2010.
- Alan Gates, “Programming Pig”, O’Reilley, 2011.
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