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Best Big Data Books

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Best Big Data Books
Best Big Data Books

Are you looking for the Best Big Data Books?… If yes, then this article is for you. In this article, you will find the Best Big Data Books for beginners & advanced like Beginner courses, and Practice test coursesSo, check these Best Big Data Books and find the best Big Data Books for Beginners to Advanced according to your need. In the previous article, I shared the Best Data Analyst Courses on DataCamp to know in 2022.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems:

In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

  • Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
  • Make informed decisions by identifying the strengths and weaknesses of different tools
  • Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
  • Understand the distributed systems research upon which modern databases are built
  • Peek behind the scenes of major online services, and learn from their architectures

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Big Data: A Revolution That Will Transform How We Live, Work, and Think:

Big Data is the first major book about this earthshaking subject, with two leading experts explaining what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards.

“An optimistic and practical look at the Big Data revolution — just the thing to get your head around the big changes already underway and the bigger changes to come.”

 Authors Viktor Mayer-Schönberger and Kenneth Cukier give an overview of what is being done with the massive amount of data that is being generated from online interaction coupled with advances in practical statistics on the analysis of this data.

The authors go through examples of how big data is being used today to give a flavour of it and then follow up the rest of the book with what is going on in the field, how it is useful, where aspects of it are going and some of the concerns we should have about our privacy.

They discuss how big data has enabled entrepreneurs to inform customers about the optimal time to buy flight tickets given that airlines vary their prices according to hidden methods that big data statistics has helped to make more sense of. The examples are a good starting point to start the discussion with the reader.

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The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios:

The Big Book of Dashboards presents a comprehensive reference for those tasked with building or overseeing the development of business dashboards.

Comprising dozens of examples that address different industries and departments (healthcare, transportation, finance, human resources, marketing, customer service, sports, etc.) and different platforms (print, desktop, tablet, smartphone, and conference room display) The Big Book of Dashboards is the only book that matches great dashboards with real-world business scenarios.

By organizing the book based on these scenarios and offering practical and effective visualization examples, The Big Book of Dashboards will be the trusted resource that you open when you need to build an effective business dashboard.

In addition to the scenarios there’s an entire section of the book that is devoted to addressing many practical and psychological factors you will encounter in your work. It’s great to have theory and evidenced-based research at your disposal, but what will you do when somebody asks you to make your dashboard ‘cooler’ by adding packed bubbles and donut charts?

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Big Data: Principles and best practices of scalable realtime data systems

Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive.

Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You’ll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you’ll learn specific technologies like Hadoop, Storm, and NoSQL databases.

This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful.

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Big Data MBA: Driving Business Strategies with Data Science:

Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business

You’ll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation.

The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization’s user experience to customers and front-end employees alike. You’ll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce.

  • Understand where and how to leverage big data
  • Integrate analytics into everyday operations
  • Structure your organization to drive analytic insights
  • Optimize processes, uncover opportunities, and stand out from the rest
  • Help business stakeholders to “think like a data scientist”
  • Understand the appropriate business application of different analytic techniques

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Big Data for the Greater Good:

This book highlights some of the most fascinating current uses, thought-provoking changes, and biggest challenges that Big Data means for our society.

The explosive growth of data and advances in Big Data analytics have created a new frontier for innovation, competition, productivity, and well-being in almost every sector of our society, as well as a source of immense economic and societal value.

From the derivation of customer feedback-based insights to fraud detection and preserving privacy; better medical treatments; agriculture and food management; and establishing low-voltage networks – many innovations for the greater good can stem from Big Data.

Given the insights it provides, this book will be of interest to both researchers in the field of Big Data, and practitioners from various fields who intend to apply Big Data technologies to improve their strategic and operational decision-making processes.

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Big Data: A Very Short Introduction

“Big Data” represents a qualitative change, not simply a quantitative one. The term refers both to the new technologies involved, and to the way it can be used by business and government. Dawn E. Holmes uses a variety of case studies to explain how data is stored, analyzed, and exploited by a variety of bodies from big companies to organizations concerned with disease control.

Big data is transforming the way businesses operate, and the way medical research can be carried out. At the same time, it raises important ethical issues; Holmes discusses cases such as the Snowden affair, data security, and domestic smart devices which can be hijacked by hackers.

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Big Data and Machine Learning in Quantitative Investment

Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance.

The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning.

  • Gain a solid reason to use machine learning
  • Frame your question using financial markets laws
  • Know your data
  • Understand how machine learning is becoming ever more sophisticated

Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.

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Simplify Big Data Analytics with Amazon EMR: A beginner’s guide to learning and implementing Amazon EMR for building data analytics solutions

This book is a practical guide to Amazon EMR for building data pipelines. You’ll start by understanding the Amazon EMR architecture, cluster nodes, features, and deployment options, along with their pricing. Next, the book covers the various big data applications that EMR supports.

You’ll then focus on the advanced configuration of EMR applications, hardware, networking, security, troubleshooting, logging, and the different SDKs and APIs it provides. Later chapters will show you how to implement common Amazon EMR use cases, including batch ETL with Spark, real-time streaming with Spark Streaming, and handling UPSERT in S3 Data Lake with Apache Hudi.

Finally, you’ll orchestrate your EMR jobs and strategize on-premises Hadoop cluster migration to EMR. In addition to this, you’ll explore best practices and cost optimization techniques while implementing your data analytics pipeline in EMR.

By the end of this book, you’ll be able to build and deploy Hadoop- or Spark-based apps on Amazon EMR and also migrate your existing on-premises Hadoop workloads to AWS.

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Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud

Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements.

Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science.

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Spark: The Definitive Guide: Big Data Processing Made Simple

Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework.

You willexplore the basic operations and common functions of Spark as structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark as scalable machine-learning library.

  • Get a gentle overview of big data and Spark
  • Learn about DataFrames, SQL, and Datasets as Spark as core APIs through worked examples
  • Dive into Spark as low-level APIs, RDDs, and execution of SQL and DataFrames
  • Understand how Spark runs on a cluster
  • Debug, monitor, and tune Spark clusters and applications
  • Learn the power of Structured Streaming, Spark as stream-processing engine
  • Learn how you can apply MLlib to a variety of problems, including classification or recommendation

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And here the list ends. So, these are the Best Big Data Books. I will keep adding more Best Big Data Books to this list.

Conclusion

I hope these Best Big Data Books will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.

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