Big Data Analytics: You can download the Study materials and notes for Big Data Analytics in PDF files from the official website.
Big Data Analytics Books
All the Books and Study materials for the B-tech program have been updated on the official website. Candidates can choose the Books they want and start preparing for the examination. Here we are going to discuss Big Data Analytics. Big data analytics is the method of analyzing vast and varied data sets - i.e., big data -- to uncover unknown correlations, hidden patterns, market trends, customer preferences and other helpful information that can help companies make more-informed business judgments.
Big Data Analytics Books 2018
With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions. Big data analytics benefits organizations harness their data and utilize it to recognize new opportunities. That, in turn, leads to active business moves, more effective operations, higher profits and satisfied customers. Data Analytics is the ability to analyze data to convert information into valuable knowledge. This knowledge could help us know our world better, and in many contexts permit us to make better decisions. While this is the general and grand objective, the last 20 years have seen steeply lowering costs to gather, store, and method data, generating an even stronger motivation for the use of practical approaches to problem-solving. Here we are providing you with the best Books and Study Materials for Python Programming language. The detailed syllabus is available on the web so that candidates can follow according to that and excel in this field.
B.Tech in Big Data Analytics
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Big Data Analytics Reference Books List
- Multidimensional Databases and Data Warehousing, Christian S. Jensen, Torben Bach Pedersen, Christian Thomsen, Morgan & Claypool Publishers, 2010
- Data Warehouse Design: Modern Principles and Methodologies, Golfarelli and Rizzi, McGraw-Hill, 2009
- Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications, Elzbieta Malinowski, Esteban Zimányi, Springer, 2008
- The Data Warehouse Lifecycle Toolkit, Kimball et al., Wiley 1998
- The Data Warehouse Toolkit, 2nd Ed., Kimball and Ross, Wiley, 2002
- Big Java 4th Edition, Cay Horstmann, Wiley John Wiley & Sons, INC
- Hadoop: The Definitive Guide by Tom White, 3rd Edition, O'Reilly
- Hadoop in Action by Chuck Lam, MANNING Publ.
- The Hadoop for Dummies by Dirk deRoos, Paul C.Zikopoulos, Roman B.Melnyk, Bruce Brown, Rafael Coss
- Hadoop in Practice by Alex Holmes, MANNING Publ.
- Hadoop MapReduce Cookbook, Srinath Perera, Thilina Gunarathne
Big Data Analytics Syllabus IIT
• Optimize business decisions and create a competitive advantage with Big Data analytics
• Introducing Java concepts required for developing map-reduce programs
• Derive business benefit from unstructured data
• Imparting the architectural concepts of Hadoop and introducing the map-reduce paradigm
• To introduce programming tools PIG & HIVE in Hadoop echo system.
UNIT – I:
Data structures in Java: Linked List, Stacks, Queues, Sets, Maps; Generics: Generic classes and Type parameters, Implementing Generic Types, Generic Methods, Wrapper Classes, Concept of
UNIT – II:
Working with Big Data: Google File System, Hadoop Distributed File System (HDFS) – Building blocks of Hadoop (Namenode, Datanode, Secondary Namenode, Job Tracker, Task Tracker), Introducing and Configuring Hadoop cluster (Local, Pseudo-distributed mode, Fully Distributed mode), Configuring XML files.
UNIT – III:
Writing MapReduce Programs: A Weather Dataset, Understanding Hadoop API for MapReduce Framework (Old and New), Basic programs of Hadoop MapReduce: Driver code, Mapper code, Reducer code, Record Reader, Combiner, Partitioner
UNIT – IV:
Hadoop I/O: The Writable Interface, Writable Comparable and comparators, Writable Classes: Writable wrappers for Java primitives, Text, Bytes Writable, Null Writable, Object Writable
and Generic Writable, Writable collections, Implementing a Custom Writable: Implementing a Raw Comparator for speed, Custom comparators
UNIT – V:
Pig: Hadoop Programming Made Easier Admiring the Pig Architecture, Going with the Pig Latin Application Flow, Working through the ABCs of Pig Latin, Evaluating Local and Distributed Modes of Running Pig Scripts, Checking out the Pig Script Interfaces, Scripting with Pig Latin
UNIT – VI:
Applying Structure to Hadoop Data with Hive: Saying Hello to Hive, Seeing How the Hive is Put Together, Getting Started with Apache Hive, Examining the Hive Clients, Working with Hive Data Types, Creating and Managing Databases and Tables, Seeing How the Hive Data Manipulation Language Works, Querying and Analyzing Data.
• Preparing for data summarization, query, and analysis.
• Applying data modeling techniques to large data sets
• Creating applications for Big Data analytics
• Building a complete business data analytic solution
Frequently Asked Questions
- Define Wrapper Class? Explain in brief about writable wrappers for java primitives.
- Differentiate between Array List and class linked list functionalities.
- What are the modes that a Hadoop can run?
- Discuss in brief about the building blocks of Hadoop?
- Describe in brief about API for the Map-reduce framework.
- Discuss in brief about the implementation of map-reduce concept with a suitable example.
- What are object writable and generic writable?
- Explain with an example, how Hadoop uses Scale-out feature to improve the performance?
- Discuss in brief about running a pig script in local and distributed mode.
- Describe in brief about PIG Commands
We have provided all the necessary materials which are needed for the preparation for your examination. Candidates can download the Books and Study materials for free in Pdf format or can purchase it directly. So without further delay candidates can download the files from the web so that they have sufficient time to prepare for the examination. Practice as many programmes as you can, because the program matters more than the theory. Make sure that you share this link with your friend sot that these books will be beneficial for them also.
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