Big Data Analytics Notes & Study Materials Pdf Download links for B.Tech Students are available here. Candidates who are pursuing Btech degree should refer to this page till to an end. Here, you can get Big Data Analytics Books Pdf Download links along with more details that are required for your effective exam preparation.
Big Data Analytics Study Materials, list of Important Questions, Big Data Analytics Syllabus, Best Recommended Books for Big Data Analytics are also available in the below modules along with the Big Data & Data Analytics Lecture Notes Download links in Pdf format. So, click on the below links and directly jump to the required info about Data Analytics & Big Data Books in PDF.
- Free Download Big Data Analytics Books & Notes PDF- Introduction
- Download Links of Big Data & Data Analytics Lecture Notes Pdf
- List of Best Reference Books for B.Tech Big Data & Data Analytics Subjects
- Big Data Analytics Syllabus for IIT Students
- List of Big Data Analytics Important Questions
- FAQs on B.Tech CSE Big Data Analytics Subject Books
Introduction to Big Data Analytics:
Data Analytics is the science of examining data to transform data into valuable insight. This information could aid us to realize our world entirely, and in various circumstances allow us to make healthier choices. While this is the large and ambitious objective, the last 20 years have seen abruptly reducing prices to collect, store, and process data, creating an equivalent more powerful urge for the use of empirical strategies to problem-solving.
This course endeavors to offer you with a wide range of data analytic methods and is structured around the broad outlines of the different types of data analytics, namely, descriptive, inferential, predictive, and prescriptive analytics. If you want to know more interesting knowledge about the course, just click on the below download links of Big Data Analytics Books & Lecture Notes pdf and gain a complete concept of it.
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Best Suggested Books can make your preparation more strong and helps you to learn a lot about the subject. So, make use of this below-provided list of Best Big Data Analytics Recommended Books and improve your knowledge to the other level about the subject to score more marks in the examination.
- The Data Warehouse Lifecycle Toolkit, Kimball et al., Wiley 1998
- Hadoop in Practice by Alex Holmes, MANNING Publ.
- Multidimensional Databases and Data Warehousing, Christian S. Jensen, Torben Bach Pedersen, Christian Thomsen, Morgan & Claypool Publishers, 2010
- Hadoop in Action by Chuck Lam, MANNING Publ.
- Data Warehouse Design: Modern Principles and Methodologies, Golfarelli and Rizzi, McGraw-Hill, 2009
- Big Java 4th Edition, Cay Horstmann, Wiley John Wiley & Sons, INC
- Advanced-Data Warehouse Design: From Conventional to Spatial and Temporal Applications, Elzbieta Malinowski, Esteban Zimányi, Springer, 2008
- The Data Warehouse Toolkit, 2nd Ed., Kimball and Ross, Wiley, 2002
- Hadoop: The Definitive Guide by Tom White, 3rd Edition, O’Reilly
- The Hadoop for Dummies by Dirk deRoos, Paul C.Zikopoulos, Roman B.Melnyk, Bruce Brown, Rafael Coss
- Hadoop MapReduce Cookbook, Srinath Perera, Thilina Gunarathne
Below is the latest Syllabus of Big Data Analytics provided for all B.Tech and IIT students to cover all the topics while exam preparation.
- Introducing Java concepts required for developing map-reduce programs
- Optimize business decisions and create a competitive advantage with Big Data analytics
- 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 Serialization
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.
Also Read: B.Tech Biotechnology Reference Books
The following questions are very important to study at the time of big data analytics exam preparation. You can also find more review questions of big data and data analytics from B.Tech Big Data Analytics Reference Books & Lecture Notes pdf which are available here in the above modules.
- Describe in brief about PIG Commands?
- Define Wrapper Class? Explain in brief about writable wrappers for java primitives.
- Explain with an example, how Hadoop uses the Scale-out feature to improve the performance?
- Differentiate between Array List and class linked list functionalities.
- Discuss in brief about the implementation of the map-reduce concept with a suitable example.
- What are the modes that a Hadoop can run?
- Describe in brief about API for the Map-reduce framework.
- What are Object writable and Generic writable?
- Discuss in brief about running a pig script in local and distributed mode.
- Discuss in brief about the building blocks of Hadoop?
1. What is Big Data Analytics and Example?
Big Data Analytics is the method of collecting, organizing and analyzing large sets of data (called Big Data) to identify patterns and other helpful information. Analysts working with Big Data usually require the knowledge that originates from analyzing the data. Some examples of enterprises that use big data analytics involve public service agencies, the hospitality industry, healthcare companies, and retail businesses.
2. How does Big Data Analytics work?
Big Data Analytics works like analyzing the large sets of data through different tools and processes to find out unique patterns, hidden correlations, meaningful trends, and other insights for creating data-driven decisions in the pursuance of better results.
3. Which is the best big data tool?
The following are the Best Big Data Tools and Software:
- Apache Storm.
4. Which tool is best for data analytics?
The following list is the Top Data Analytics Tools available in both open-source and paid versions, which depends on their popularity, learning, and performance.
- R Programming
- Tableau Public
- Apache Spark
5. Why is Big Data Analytics important?
Big data analytics helps organizations control their data and use it to identify new chances. That, in turn, drives to smarter business moves, more efficient operations, higher profits, and happier clients.
6. Which is the best book for Big Data Analytics Subject?
Here are the 6 best Big Data Analytics Books & Notes, important for the students to secure max. marks in the semester exam:
- Big Java 4th Edition, Cay Horstmann, Wiley John Wiley & Sons, INC
- Hadoop – The Definitive Guide by Tom White.
- Hadoop for Dummies by Dirk Deroos.
- Map Reduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop
- Hadoop by Donald Miner.
- Hadoop Operations by Eric Sammers.
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