Big Data Analytics for GTU 18 Course (VI- IT/Prof. Elec.-II - 3161607) & (VII - CE/CSE/Professional Elective - VI)

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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. (Chapter - 1) 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, 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, security in Hadoop, Administering Hadoop, Monitoring-Maintenance, Hadoop benchmarks, Hadoop in the cloud. (Chapter - 2) 3. NoSQL 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 problems. (Chapter - 3) 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. (Chapter - 4) 5. Frameworks 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. (Chapter - 5) 6. Spark Introduction to Data Analysis with Spark, In-Memory Computing with Spark, Spark Basics, Interactive Spark with PySpark, Writing Spark Applications. (Chapter - 6)

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Pages: 208 Edition: 2023 Vendors: Technical Publications