Best Big Data Books that You Can Buy Today
This complete book has created by the open-source cluster-computing framework's creators will teach you how to use, implement, and maintain Apache Spark. This book will be covered machine learning approaches and scenarios for using a Machine learning library. Get a basic understanding of big data with it. Worked examples will teach you about Datasets, SQL, and Datasets, three of fundamental APIs.
You Will Also Acquire:
A basic understanding of big data via Spark.
Worked examples will teach you about Data Frames, SQL, and Datasets, three of fundamental APIs.
Explore low-level APIs, RDDs, and SQL and Data Frame execution.
Learn how it operates in a cluster.
Spark clusters and applications can be debugged, monitored, and tuned.
Discover the power of Spark's stream-processing engine, Structured Streaming.
Discover how to use MLlib to solve a variety of problems, like categorization and recommendation.
Data science and big data analytics are about harnessing the power of data to generate new insights. This book will cover a wide range of activities, methods, and tools used by data scientists. Content is focused on concepts, principles, and practical applications applicable to all industrial and technological environments illustrated with examples to aid learning and replicate using open-source software.
This Book Will Help to Learn:
Deploy a structured lifecycle approach to solving data analytics challenges appropriate analysis techniques and tools for big data analysis
Learn how to use data to tell compelling stories to drive your business
Prepare for EMC Proven Professional Data Science Certification·
Discover, analyze, visualize and present your data in a meaningful way, starting today!
3. Big Data Fundamentals: Concepts, Drivers & Techniques (The Pearson Service Technology Series from Thomas Erl)
Big Data Fundamentals is a practical introduction to Big Data. Thomas Erl and his team, best-selling IT authors, teach core Big Data concepts, theory, and terminology, as well as fundamental technologies and methodologies. All of the information is backed up with case studies and a slew of easy graphics.
The authors begin by describing how Big Data can help a company advance by solving a wide range of previously intractable business difficulties. They also explain how to build and integrate a Big Data solution environment to gain competitive advantages by essential analysis of techniques and technologies.
This Book Can Assist You With:
Understanding the underlying concepts of Big Data and how it differs from other types of data analysis and data science
From operational gains to innovation, understanding the business motives and forces driving Big Data adoption is critical.
Big Data projects that are strategic and business-driven are being planned.
Data management, governance, and security are just a few of the issues that need to be addressed.
Clarifying the connections between Big Data and OLTP, OLAP, ETL, data warehouses, and data marts
Using organized, unstructured, semi-structured, and metadata forms to work with Big Data
Integrating Big Data resources with corporate performance monitoring to increase value
Understanding how distributed and parallel computing are used in Big Data
Using NoSQL and other technologies to suit the unique data processing needs of Big Data
4. Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools
The first approach to good data science, data analysis, and machine learning is to clean the data. This book is your go-to resource if you work with any form of data, empowering you with the insights and heuristics that seasoned data scientists had to learn the hard way.
Python veteran David Mertz explains to you the ins and outs of data preparation and the important questions you should be asking of every piece of data you work within a light-hearted and fascinating study of diverse tools, methodologies, and datasets real and fake.
Cleaning Data for Effective Data Science explores the data cleaning pipeline from start to finish, focusing on helping you comprehend the principles underlying it. It uses a blend of Python, R, and standard command-line tools.
What You'll Discover:
Common data formats such as JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structures are ingested and worked with.
Learn why and how we use technologies like pandas, SciPy, Scikit-learn, Tidyverse, and Bash.
Use relevant principles and heuristics, such as Benford's law and the 68-95-99.7 rule, to assess data quality and uncover bias.
Evaluate the z-score and other statistical features to identify and handle erroneous data and outliers.
To correct imbalances, impute sensible values to missing data, and use sampling.
To bring out patterns in your data, use dimensionality reduction, quantization, one-hot encoding, and other feature engineering approaches.
De-trending and interpolation should be considered with care when working with time-series data.
5. Big Data Platforms and Applications: Case Studies, Methods, Techniques, and Performance Evaluation (Computer Communications and Networks)
With a focus on methods, tactics, and performance assessment, this book provides a survey of advanced digital asset management to theory, research, analysis, and implementation in the context of big data platforms and their applications.
The regular growth in the volume, speed and variety of data created necessitates a steady rise in server and network infrastructure processing speeds, as well as new resource management techniques. This presents huge difficulties (as well as exciting future opportunities) for data-intensive and high-performance computing, i.e., how to efficiently convert extremely massive datasets into relevant information and knowledge.
You Will However Benefit from This Book :
The process of managing context data is made more difficult by the range of sources from which it originates, resulting in a variety of data formats with differing storage, transformation, delivery, and archival needs.
Real-time applications, on the other hand, necessitate quick reactions. Because the whole application performance is heavily dependent on the features of the data management service, providing highly scalable data management in such contexts has become a significant problem with the rise of cloud infrastructures.
6. Big Data Processing with Apache Spark: Efficiently tackle large datasets and big data analysis with Spark and Python
Due to scalability, information integrity, and fault-tolerance, processing massive data in real-time is difficult. This book will show you how to utilize Spark to improve the speed and efficiency of your whole analytical workflow. You'll learn about Spark Streaming, the Spark Streaming API, machine learning extensions, and structured streaming, as well as other key concepts and technologies in the Spark ecosystem.
The Resilient Distributed Datasets (RDDs), SQL, Datasets, and Dataframes APIs will be used to teach you the fundamentals of data processing. After mastering these principles, you'll learn how to use Spark Streaming APIs to consume data from TCP sockets in real-time, as well as how to integrate Amazon Web Services (AWS) for stream consumption. You'll not only know how to use machine learning extensions and structured streams by the end of this book, but you'll also be able to use Spark in your perspective on the project of this field.
What You'll Discover :
Build Python applications that communicate with Spark on your own.
Utilize Apache Spark to consume data streams.
Recognize typical Spark operations for processing well-known data streams.
Connect Spark streaming to Amazon Web Services (AWS) (AWS).
With the dataset, create a collaborative filtering model.
Apply Spark machine learning APIs to processed data streams.
7. Limitless Analytics with Azure Synapse: An end-to-end analytics service for data processing, management, and ingestion for BI and ML requirements
Microsoft describes Azure Synapse Analytics as the next evolution of Azure SQL Data Warehouse. It is a limitless analytics service that combines enterprise data warehousing and big data analytics. This book will teach you how to use this platform to effectively discover insights from your data.
The book begins with an overview of Azure Synapse Analytics, including its architecture and how it can be used to enhance business intelligence and machine learning capabilities. After that, you'll select and configure the appropriate environment for your business challenge. You'll also learn how to ingest data from a range of sources and orchestrate the data using Azure Synapse's transformation mechanisms. Eventually, you'll learn how to use SQL to manage both relational and non-relational data. As you proceed, you'll use several languages to do real-time streaming and data analysis operations on your data before applying machine learning techniques to obtain accurate and granular insights from data. Finally, you'll learn how to use security and privacy tools to secure critical data in real-time.
You'll be capable of constructing peer-to-peer analytics solutions while focusing on data prep, data management, data warehousing, and AI activities by the end of this Azure book.
The practice of studying vast and complicated data sets that often exceed processing capabilities is known as big data analytics. R is a popular data science programming language with a wide range of tools for dealing with all types of Big Data issues.
This is the best big data books if you want to learn practically. The book starts with a quick introduction to the Big Data world and current industry standards, followed by an overview of the R language, including it's history, structure, real-world applications, and flaws. The book then moves on to a review of some of the most important R functions for data management and transformations. You'll learn about cloud-based Big Data solutions (such as Amazon EC2 instances and Amazon RDS, Microsoft Azure, and its HDInsight clusters), as well as how to link R to all of this.
What You'll Discover:
- Learn how to use the R programming language and its extensive statistical capabilities to learn about the current status of Big Data.
- Cost-effectively and quickly deploy Big Data analytics systems with selected Big Data technologies enabled by R.
- On a multi-node Hadoop cluster, apply the R language to real-world Big Data challenges, such as electricity consumption across various socio-demographic characteristics and bike-share scheme utilization.
- Investigate R's interoperability with Hadoop, Spark, SQL, and NoSQL databases, as well as the H2O platform.
This book discusses the ethical issues posed by the big data phenomena and discusses why businesses should reexamine their privacy and identification policies. Kord Davis and Doug Patterson, the authors, offer methodologies and tactics to assist your company in conducting a transparent and productive ethical investigation into its existing data.
Individuals and businesses alike have a legitimate interest in learning how data is managed. As Target, Apple, Netflix, and hundreds of other firms have discovered, how you use data may have a direct impact on brand quality and profitability. This book will teach you how to connect your activities with your company's explicit principles and maintain customer, partner, and stakeholder trust.
You will learn:
Examine your data-handling procedures to see if they represent your company's basic principles.
Make clear and consistent statements about your company's usage of big data.
Define tactical approaches to bridge the gap between values and practices—and figure out how to keep them aligned when circumstances change.
Maintain a healthy balance between the advantages of innovation and the dangers of unexpected consequences.
A stunning examination of the most recent technological development and its far-reaching implications for the economy, science, and wider society.
Which hue of paint is most likely to indicate that a secondhand car is in good condition? How can officials locate the most dangerous manholes in New York City before they explode? And how did Google searches for the H1N1 flu outbreak forecast its spread?
Big data is the key to answering these and other problems. The term "big data" means analyzing a large amount of data and sometimes derive conclusions. This new science can convert a wide range of occurrences into searchable form, from airline ticket prices to the content of millions of books, and it can harness our growing computational power to find epiphanies that we never could have noticed before. Big data will revolutionize the way we think about business, health, politics, education, and innovation in the years to come, on par with the Internet or perhaps even the printing press. It also brings new dangers, ranging from the inescapable end of privacy as we know it to the possibility of being punished for things we haven't done yet, thanks to big data's ability to forecast our future action.
Two leading experts explain what big data is, how it will transform our lives, and what we can do to protect ourselves from its dangers in this stunningly clear, often unexpected book. The first big book about the next great thing is Big Data.
From an integrative viewpoint, this big data book gives new insight into a variety of big data issues. On the one hand, it covers legal, sociological, and economic approaches to core big data concerns including privacy, data quality, and the European Court of Justice's Safe Harbor judgment, as well as practical applications like smart cars, wearables, and web monitoring. It gives a detailed overview of and introduction to the increasing difficulties around big data, addressing the interests of both researchers and practitioners.
As part of the ABIDA project, this book was created (Assessing Big Data, 01IS15016A-F). The Federal Ministry of Education and Research is funding ABIDA, a four-year cooperation effort. The ideas and opinions presented in this book, however, are solely those of the authors and do not necessarily reflect the views of all members of the ABIDA project or the Federal Ministry of Education and Research.
12. Big Data: Using Smart Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance
Be smart to turn big data promises into real results. There is a lot of turmoil about big data. It is important for us to know the working functions of this.
But what sets it apart is that you know how to use big data to drive solid and realistic business outcomes and use them to improve performance. Big Data shows how to implement the same techniques that big companies have used to achieve new levels of profitability. With clear explanations and countless examples, you'll learn how successful companies, big and small, can move forward with the SMART model.
Big Data At Work covers all the foundations of this field. What big data means in terms of technology, consumers, and management. What are the opportunities and costs? If it can affect the actual business.
By Reading This Book You Will Understand:
- Why has Big Data important for you and your organisation?
- What kind of technology do you need to manage it.
- How can Big Data be changed your job, business, and industry.
- How to hire, hire or develop the types of people that will make big data work.
- How Big Data has driven a new approach to Analytical Management.