Big Data for Beginners: Data at Scale. Harnessing the Potential of Big Data Analytics

By Tom Lesley

Big Data: Data at Scale. Harnessing the Potential of Big Data Analytics is a comprehensive guide to understanding and utilizing the power of Big Data analytics. In today's digital age, the amount of data generated is increasing at an unprecedented rate, and traditional approaches to data management and analysis are no longer sufficient. This book offers insights into the characteristics, challenges, and opportunities of Big Data, as well as an overview of the technologies and tools used to manage and analyze it.

 

The book delves into the basics of Big Data analytics, including descriptive analytics and data visualization, predictive analytics and machine learning, prescriptive analytics and optimization, and real-time analytics and stream processing. It also explores the applications of Big Data analytics in business, healthcare, finance, government, and science and research.

 

In addition to discussing the opportunities presented by Big Data analytics, the book also addresses the importance of ethics and privacy in data management, and provides best practices and regulations for ensuring ethical and responsible use of data. The future of Big Data is also explored, including trends and developments, the role of Big Data in digital transformation, and its intersection with artificial intelligence.

 

Data at Scale is a must-read for anyone interested in understanding the power of Big Data analytics and leveraging it to drive business value, achieve better healthcare outcomes, or advance scientific research. The book is written in an accessible style, with clear explanations and real-world examples, making it ideal for both technical and non-technical readers alike.

• Language: English
• Publisher: May Reads
• Release date: Apr 19, 2024
• ISBN: 9798224167371

Book preview

Tom Lesley

Table of Content

Part 1: Introduction to Big Data

Chapter 1: The Rise of Big Data

Chapter 2: Understanding the Characteristics of Big Data

Chapter 3: The Challenges and Opportunities of Big Data

Part 2: Big Data Technologies and Tools

Chapter 4: An Overview of Big Data Technologies and Tools

Chapter 5: Hadoop and the Hadoop Ecosystem

Chapter 6: Spark and the Spark Ecosystem

Chapter 7: NoSQL Databases and Other Big Data Technologies

Part 3: Big Data Analytics

Chapter 8: The Basics of Big Data Analytics

Chapter 9: Descriptive Analytics and Data Visualization

Chapter 10: Predictive Analytics and Machine Learning

Chapter 11: Prescriptive Analytics and Optimization

Chapter 12: Real-time Analytics and Stream Processing

Part 4: Applications of Big Data Analytics

Chapter 13: Big Data Analytics in Business

Chapter 14: Big Data Analytics in Healthcare

Chapter 15: Big Data Analytics in Finance

Chapter 16: Big Data Analytics in Government

Chapter 17: Big Data Analytics in Science and Research

Part 5: Big Data Ethics and Privacy

Chapter 18: The Importance of Ethics and Privacy in Big Data

Chapter 19: Risks and Challenges in Big Data Ethics and Privacy

Chapter 20: Best Practices and Regulations for Big Data Ethics and

Privacy

Part 6: The Future of Big Data

Chapter 21: Trends and Developments in Big Data Analytics

Chapter 22: Big Data and Artificial Intelligence

Chapter 23: The Role of Big Data in Digital Transformation

Conclusion: Harnessing the Power of Big Data Analytics

Chapter 24: Success Stories of Big Data Analytics

Chapter 25: Challenges and Opportunities in Big Data Analytics

Chapter 26: The Future of Big Data and the Opportunities It Presents

Big Data for Beginners

Data at Scale. Harnessing the Potential of Big Data Analytics

Tom Lesley

Part 1: Introduction to Big Data

Chapter 1: The Rise of Big Data

The rise of big data refers to the exponential growth in the volume, variety, and velocity of data that has been generated in recent years. This growth has been driven by a number of factors, including the increasing use of digital devices and the proliferation of the internet and social media.

The term big data is used to describe datasets that are so large and complex that traditional data processing techniques are insufficient to handle them. These datasets can come from a wide variety of sources, including social media, internet search queries, financial transactions, and sensor data from the Internet of Things (IoT).

One of the key challenges associated with big data is the need for new tools and technologies to store, process, and analyze these vast datasets. This has led to the development of new technologies such as Hadoop and Spark, which are designed specifically to handle big data.

The rise of big data has had a significant impact on many different industries, including healthcare, finance, retail, and manufacturing. It has also created new opportunities for businesses to gain insights into consumer behavior and to develop more targeted marketing strategies.

However, the rise of big data has also raised a number of concerns around privacy and security. As more and more