Big Data In Practice PDF Free Download

10/4/2021by admin

Big Data will give you a clear understanding, blueprint, and step-by-step approach to building your own big data strategy. This is a well-needed practical introduction to actually putting the topic into practice. May 14, 2020 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. Big Data is not a technology related to business transformation; instead, it enables innovation within an enterprise on the condition that the enter-prise acts upon its insights. Chapter 3 shows that Big Data is not simply “business as usual,” and that the decision to adopt Big Data must take into account many business and technol. Visualising big data in R April 2013 Birmingham R User Meeting Alastair Sanderson 23rd April 2013 The challenge of visualising big data Only a few million pixels on a screen.

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.

Big Data Analytics Lecture Notes PDF Free Download

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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Download Links of Big Data & Data Analytics Books Pdf

Books & NotesDownload Links
Big Data Analytics Syllabus PDFDownload
Best Data Analytics & Big Data Books for BeginnersDownload
Big Data Analytics Lecture Notes PDFDownload
Big Data & Data Analytics Question Paper PDFDownload

Big Data Analytics Reference Books List

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

Big Data Analytics Syllabus for IIT Students

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.


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


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.


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


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


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


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

List of B.Tech & IIT Big Data Analytics Review Questions

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.

  1. Describe in brief about PIG Commands?
  2. Define Wrapper Class? Explain in brief about writable wrappers for java primitives.
  3. Explain with an example, how Hadoop uses the Scale-out feature to improve the performance?
  4. Differentiate between Array List and class linked list functionalities.
  5. Discuss in brief about the implementation of the map-reduce concept with a suitable example.
  6. What are the modes that a Hadoop can run?
  7. Describe in brief about API for the Map-reduce framework.
  8. What are Object writable and Generic writable?
  9. Discuss in brief about running a pig script in local and distributed mode.
  10. Discuss in brief about the building blocks of Hadoop?

FAQs on B.Tech CSE Big Data and Data Analytics Courses Books

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.
  • Hadoop.
  • MongoDB.
  • Quoble.
  • Cassandra.
  • CouchDB.
  • HPCC.
  • Statwing.

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.

  • SAS
  • QlikView
  • R Programming
  • Tableau Public
  • RapidMiner
  • Excel
  • 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:

  1. Big Java 4th Edition, Cay Horstmann, Wiley John Wiley & Sons, INC
  2. Hadoop – The Definitive Guide by Tom White.
  3. Hadoop for Dummies by Dirk Deroos.
  4. Map Reduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop
  5. Hadoop by Donald Miner.
  6. Hadoop Operations by Eric Sammers.


I hope the data shared above regarding Big Data Analytics B.Tech subject Notes & Books Pdf is very helpful for the students who are passionate to learn about the subject in depth. Also, you can find more details like syllabus, review questions, best reference books, etc. along with Big Data Analytics Lecture Notes Pdf Download links.

So, refer to this article completely & Download Big Data Analytics Books in Pdf. Moreover, share this article with your friends and help them during their exam preparation. Bookmark our site and get more details about the same & other related info on courses, syllabus, exams.

Today's market is flooded with an array of Big Data tools and technologies. They bring cost efficiency, better time management into the data analytical tasks.

Here is the list of best big data tools and technologies with their key features and download links. This big data tools list includes handpicked tools and softwares for big data.

Best Big Data Tools and Software

Atlas.ti30-Days Free Trial + Paid Plan

1) Hadoop:

The Apache Hadoop software library is a big data framework. It allows distributed processing of large data sets across clusters of computers. It is one of the best big data tools designed to scale up from single servers to thousands of machines.


  • Authentication improvements when using HTTP proxy server
  • Specification for Hadoop Compatible Filesystem effort
  • Support for POSIX-style filesystem extended attributes
  • It has big data technologies and tools that offers robust ecosystem that is well suited to meet the analytical needs of developer
  • It brings Flexibility In Data Processing
  • It allows for faster data Processing

Download link:

2) Atlas.ti

Atlas.ti is all-in-one research software. This big data analytic tool gives you all-in-one access to the entire range of platforms. You can use it for qualitative data analysis and mixed methods research in academic, market, and user experience research.


  • You can export information on each source of data.
  • It offers an integrated way of working with your data.
  • Allows you to rename a Code in the Margin Area
  • Helps you to handle projects that contain thousands of documents and coded data segments.

3) HPCC:

HPCC is a big data tool developed by LexisNexis Risk Solution. It delivers on a single platform, a single architecture and a single programming language for data processing.


  • It is one of the Highly efficient big data tools that accomplish big data tasks with far less code.
  • It is one of the big data processing tools which offers high redundancy and availability
  • It can be used both for complex data processing on a Thor cluster
  • Graphical IDE for simplifies development, testing and debugging
  • It automatically optimizes code for parallel processing
  • Provide enhance scalability and performance
  • ECL code compiles into optimized C++, and it can also extend using C++ libraries

Download link:

4) Storm:

Storm is a free big data open source computation system. It is one of the best big data tools which offers distributed real-time, fault-tolerant processing system. With real-time computation capabilities.


  • It is one of the best tool from big data tools list which is benchmarked as processing one million 100 byte messages per second per node
  • It has big data technologies and tools that uses parallel calculations that run across a cluster of machines
  • It will automatically restart in case a node dies. The worker will be restarted on another node
  • Storm guarantees that each unit of data will be processed at least once or exactly once
  • Once deployed Storm is surely easiest tool for Bigdata analysis

Download link:

Sample Big Data Download

5) Qubole:

Qubole Data is Autonomous Big data management platform. It is a big data open source tool which is self-managed, self-optimizing and allows the data team to focus on business outcomes.


  • Single Platform for every use case
  • It is an Open-source big data software having Engines, optimized for the Cloud
  • Comprehensive Security, Governance, and Compliance
  • Provides actionable Alerts, Insights, and Recommendations to optimize reliability, performance, and costs
  • Automatically enacts policies to avoid performing repetitive manual actions

Download link:

6) Cassandra:

The Apache Cassandra database is widely used today to provide an effective management of large amounts of data.


  • Support for replicating across multiple data centers by providing lower latency for users
  • Data is automatically replicated to multiple nodes for fault-tolerance
  • It one of the best big data tools which is most suitable for applications that can't afford to lose data, even when an entire data center is down
  • Cassandra offers support contracts and services are available from third parties

Download link:

7) Statwing:

Statwing is an easy-to-use statistical tool. It was built by and for big data analysts. Its modern interface chooses statistical tests automatically.


  • It is a big data software that can explore any data in seconds
  • Statwing helps to clean data, explore relationships, and create charts in minutes
  • It allows creating histograms, scatterplots, heatmaps, and bar charts that export to Excel or PowerPoint
  • It also translates results into plain English, so analysts unfamiliar with statistical analysis

Download link:

8) CouchDB:

CouchDB stores data in JSON documents that can be accessed web or query using JavaScript. It offers distributed scaling with fault-tolerant storage. It allows accessing data by defining the Couch Replication Protocol.


  • CouchDB is a single-node database that works like any other database
  • It is one of the big data processing tools that allows running a single logical database server on any number of servers
  • It makes use of the ubiquitous HTTP protocol and JSON data format
  • Easy replication of a database across multiple server instances
  • Easy interface for document insertion, updates, retrieval and deletion
  • JSON-based document format can be translatable across different languages

Download link:

9) Pentaho:

Pentaho provides big data tools to extract, prepare and blend data. It offers visualizations and analytics that change the way to run any business. This Big data tool allows turning big data into big insights.


  • Data access and integration for effective data visualization
  • It is a big data software that empowers users to architect big data at the source and stream them for accurate analytics
  • Seamlessly switch or combine data processing with in-cluster execution to get maximum processing
  • Allow checking data with easy access to analytics, including charts, visualizations, and reporting
  • Supports wide spectrum of big data sources by offering unique capabilities

Download link:

Big Data In Practice PDF Free Download

10) Flink:

Apache Flink is one of the best open source data analytics tools for stream processing big data. It is distributed, high-performing, always-available, and accurate data streaming applications.


  • Provides results that are accurate, even for out-of-order or late-arriving data
  • It is stateful and fault-tolerant and can recover from failures
  • It is a big data analytics software which can perform at a large scale, running on thousands of nodes
  • Has good throughput and latency characteristics
  • This big data tool supports stream processing and windowing with event time semantics
  • It supports flexible windowing based on time, count, or sessions tos SQL-inspired language separates the user from the complexity of Map Reduce programming
  • It offers Java Database Connectivity (JDBC) interface

Download link:


💻 What is Big Data Software?

Big data software is used to extract information from a large number of data sets and processing these complex data. A large amount of data is very difficult to process in traditional databases. so that's why we can use this tool and manage our data very easily.

🚀 Which are the Best Big Data Tools?

Below are some of the Best Big Data Tools:

  • Hadoop
  • Atlas.ti
  • HPCC
  • Storm
  • Qubole
  • Cassandra
  • Statwing
  • CouchDB
Big Data In Practice PDF Free Download

⚡ Which factors should you consider while selecting a Big Data Tool?


Big Data In Practice Pdf Free Download 2020

You should consider the following factors before selecting a Big Data tool

Big Data In Practice Pdf Free Download 64 Bit

  • License Cost if applicable
  • Quality of Customer support
  • The cost involved in training employees on the tool
  • Software requirements of the Big data Tool
  • Support and Update policy of the Big Data tool vendor.
  • Reviews of the company
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