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Konieczny B: Data Engineering Design
Patterns

Data Engineering Design Patterns

Recipes for Solving the Most Common Data Engineering Problems

Transforming Global Healthcare: The Role of AI and IoT in Modern Medical Systems explores the transformative potential of the integration of technologies such as AI, Big Data, IoT, and Blockchain in the healthcare sector. This comprehensive volume aims to illustrate how these advanced technologies can address sustainability challenges, improve decision-making processes, and enhance overall healthcare delivery. By mapping existing knowledge and forecasting future trends, the book provides a detailed roadmap for sustainable healthcare development.Key features include: - Explores AI, big data, IoT, and blockchain in healthcare in-depth. - Provides actionable strategies for energy management, waste disposal, and green supply chains - Provides in-depth coverage of real-time health monitoring, predictive analytics, robotic surgery, and AI medical imaging. - Provides insights into preventing drug counterfeiting, managing pharmaceutical cold chains, and optimizing supply chains. - Comprises a comparative analysis of global healthcare systems, telemedicine evolution, and future digital health trends.

The Role of AI and IoT in Modern Medical Systems

Transforming Global Healthcare

The accelerating global demand for sustainable solutions has catalyzed the integration of emerging technologies with sustainability-driven innovations. This book explores the intersection of technology, engineering, and environmental responsibility through six critical themes: Artificial Intelligence and Machine Learning for Sustainability, Cloud Computing Technologies, Sustainable Energy Technologies, Sustainable Materials and the Built Environment, Sustainable Actions, and Innovative Engineering and Technology.Artificial intelligence and machine learning play a pivotal role in optimizing resource management and enabling low-carbon decision-making, while cloud computing technologies provide scalable platforms for collaboration and innovation. Sustainable energy technologies such as solar, wind, and bioenergy are examined as vital pathways toward clean power generation. The discussion extends to sustainable materials and the built environment, emphasizing eco-friendly design and circular economy principles. The book also highlights sustainable actions driven by policy, behavioral change, and community engagement. Together, these domains represent a multidisciplinary pathway toward achieving global sustainability goals, fostering a balance between technological advancement and ecological preservation.This book is a key resource for leaders, academia, policymakers, professionals, and researchers to advance sustainable development through science, technology, and engineering. It provides valuable insights for integrating AI, renewable energy, and sustainable engineering into practical applications for a greener future.

Emerging Frontiers in Sustainable Energy, Smart Systems, and Green Technology

The book presents real-life applications of Data Science and Big Data for Sustainable Development Goals. It includes a list of case studies from different regions across India. The book uses both structured and unstructured data like numeric data, textual data, video/image data, etc., across the various chapters for the analysis. It further delves into various data science techniques, starting from data collection on the ground in the unavailability of data to a dashboard, reporting, unsupervised methods like clustering, artificial intelligence, machine learning and deep learning, search or information retrieval, time series forecasting, optimisation, etc.¿ Showcase data science decision-making processes, driving innovation, and solving complex problems in real-case scenarios from across sectors like finance, healthcare, and e-commerce.¿ The SDGs provide a framework for societal development and well-being for all; the data science and big data interventions in this book are aligned towards mapping the various SDG goals.¿ Most of the data science use cases and initiatives projects covered in this book have been implemented by the central or state governments across different states of India.¿ Shows how data science intervention can transform the social sector, potentially driving positive change and addressing critical societal challenges.¿ Explained the fundamentals of data science theories, including concepts like classification, classification, regression, predictive analytics, optimisation, artificial intelligence, deep learning, time series forecasting, etc. for readers of different disciplines.It serves as a valuable reference for graduate students, researchers, and scholars seeking to deepen their knowledge and engage with real life applications of Data Science. This will also serve as a valuable resource for government officers and policy practitioners, providing a range of cases on the use of data-based methods for improving governance and policy making.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC BY-NC-ND)] 4.0 license funded by by the Great Lakes Institute of Management, Gurgaon Campus, Delhi NCR, India.

Indian Case Studies

Data Science for Sustainable Development Goals

Data Pipelines with Apache Airflow has empowered thousands of data engineers to build more successful data platforms. This new second edition has been fully revised for Airflow 3 with coverage of all the latest features of Apache Airflow, including the Taskflow API, deferrable operators, and Large Language Model integration. Filled with real-world scenarios and examples, you'll be carefully guided from Airflow novice to expert. &gt; In Data Pipelines with Apache Airflow, Second Edition you'll learn how to: • Master the core concepts of Airflow architecture and workflow design • Schedule data pipelines using the Dataset API and time tables, including complex irregular schedules • Develop custom Airflow components for your specific needs • Implement comprehensive testing strategies for your pipelines • Apply industry best practices for building and maintaining Airflow workflows • Deploy and operate Airflow in production environments • Orchestrate workflows in container-native environments • Build and deploy Machine Learning and Generative AI models using Airflow About the Technology Apache Airflow provides a unified platform for collecting, consolidating, cleaning, and analyzing data. With its easy-to-use UI, powerful scheduling and monitoring features, plug-and-play options, and flexible Python scripting, Airflow makes it easy to implement secure, consistent pipelines for any data or AI task. About the book Data Pipelines with Apache Airflow, Second Edition teaches you how to build, monitor, and maintain effective data workflows. This new edition adds comprehensive coverage of Airflow 3 features, such as event-driven scheduling, dynamic task mapping, DAG versioning, and Airflow’s entirely new UI. The numerous examples address common use cases like data ingestion and transformation and connecting to multiple data sources, along with AI-aware techniques such as building RAG systems. What's inside • Deploying data pipelines as Airflow DAGs • Time and event-based scheduling strategies • Integrating with databases, LLMs, and AI models • Deploying Airflow using Kubernetes About the reader For data engineers, machine learning engineers, DevOps, and sysadmins with intermediate Python skills. About the author Julian de Ruiter, Ismael Cabral, Kris Geusebroek, Daniel van der Ende, and Bas Harenslak are seasoned data engineers and Airflow experts. Table of Contents Part 1 1 Meet Apache Airflow 2 Anatomy of an Airflow DAG 3 Time-based scheduling 4 Asset-aware scheduling 5 Templating tasks using the Airflow context 6 Defining dependencies between tasks Part 2 7 Triggering workflows with external input 8 Communicating with external systems 9 Extending Airflow with custom operators and sensors 10 Testing 11 Running tasks in containers Part 3 12 Best practices 13 Project: Finding the fastest way to get around NYC 14 Project: Keeping family traditions alive with Airflow and generative AI Part 4 15 Operating Airflow in production 16 Securing Airflow 17 Airflow deployment options A Running code samples B Prometheus metric mapping

Orchestration for Data and AI

Data Pipelines with Apache Airflow, Second Edition

Demystifying Technologies for Computational Excellence

Artificial intelligence (AI) technology is rapidly gaining popularity across various industries, not just in large corporations but also in our everyday lives. The healthcare sector has particularly benefited from the advancements in AI, as it has revolutionized the way diagnostic information is compiled and utilized to understand and treat diseases.Artificial Intelligence Methods and Applications Enabling Health 5.0 discusses the cutting-edge AI technology that is revolutionizing healthcare education. It explores how AI can enhance, broaden, and update the current healthcare standard, as it uncovers uncharted territory in the quest for the best applications of AI in healthcare. The book synthesizes recent advancements in healthcare decision-making and prediction, which are reshaping the focus of AI in this field. In addition, the book covers how AI is revolutionizing disease diagnosis and treatment within the medical community, providing unparalleled accuracy and efficiency. Lastly, real-time case studies and innovative work are included to offer a glimpse into the future possibilities and scope of AI in healthcare.This book will appeal to a wide range of readers, including those in healthcare, manufacturing, e-commerce, and automation, among others. Technology enthusiasts will also discover valuable insights into the significant progress being made in this field. There remains a constant pursuit of optimal solutions for real-time applications and this book aims to provide answers to this ongoing quest for innovation.

Artificial Intelligence Methods and Applications Enabling Healthcare 5.0

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Build AI Models from Scratch (No PhD Required) Deep Learning Crash Course is a fast-paced, thorough introduction that will have you building today’s most powerful AI models from scratch. No experience with deep learning required! Designed for programmers who may be new to deep learning, this book offers practical, hands-on experience, not just an abstract understanding of theory. You’ll start from the basics, and using PyTorch with real datasets, you’ll quickly progress from your first neural network to advanced architectures like convolutional neural networks (CNNs), transformers, diffusion models, and graph neural networks (GNNs). Each project can be run on your own hardware or in the cloud, with annotated code available on GitHub. You’ll build and train models to:&nbsp; <ul><li>Classify and analyze images, sequences, and time series</li><li>Generate and transform data with autoencoders, GANs (generative adversarial networks), and diffusion models</li><li>Process natural language with recurrent neural networks and transformers</li><li>Model molecules and physical systems with graph neural networks</li><li>Improve continuously through reinforcement and active learning</li><li>Predict chaotic systems with reservoir computing</li></ul> Whether you’re an engineer, scientist, or professional developer, you’ll gain fluency in deep learning and the confidence to apply it to ambitious, real-world problems. With Deep Learning Crash Course, you’ll move from using AI tools to creating them.

Deep Learning Crash Course

This updated edition provides an essential how-to guide on the application of structural equation modeling (SEM) techniques with the AMOS software, focusing on the practical applications of both simple and advanced topics.Written in an easy-to-understand conversational style, the book covers everything from data collection and screening to confirmatory factor analysis, structural model analysis, mediation, moderation, and more advanced topics such as mixture modeling, censored date, and non-recursive models. Through step-by-step instructions, screen shots, and suggested guidelines for reporting, Collier cuts through abstract definitional perspectives to give insight on how to actually run analysis. Unlike other SEM books, the examples used will often start in SPSS and then transition to AMOS so that the reader can have full confidence in running the analysis from beginning to end. Best practices are also included on topics like how to determine if your SEM model is formative or reflective, making it not just an explanation of SEM topics, but a guide for researchers on how to develop a strong methodology while studying their respective phenomenon of interest. With a focus on practical applications of both basic and advanced topics, and with detailed work-through examples throughout, this book is ideal for experienced researchers and beginners across the behavioral and social sciences.

Basic to Advanced Techniques

Applied Structural Equation Modeling Using AMOS

In today's digital age, companies need to adopt cutting-edge artificial intelligence solutions to effectively detect and counter potential threats. This handbook brings together a team of experts to discuss insights on proactive strategies, threat mitigation techniques, and comprehensive tactics for safeguarding sensitive data.

A Holistic Approach to Security

Handbook of AI-Driven Threat Detection and Prevention

Recursions

A Media Theory of Machine Learning

Errant Intelligence

'Engaging, insightful, panoramic' Mustafa Suleyman, CEO of Microsoft AI &amp; Cofounder of DeepMind 'An eye-opening exploration of a revolution unfolding before our eyes' New York Journal of Books Stunning advances in digital technology have introduced a new wave of human-like AI systems. Chatbots like ChatGPT, Claude, and Gemini are already reshaping economies, challenge democracies, and reshaping society in unpredictable ways. And soon, these AI systems could make autonomous decisions on their users' behalf, transforming everything we do. Understanding how they work is crucial. Can AI systems think, know, and understand?Could they manipulate or deceive you, and if so, what might they make you do? Whose interests do they represent? When will they be able to move beyond words and take action in the real world? Neuroscientist and AI researcher Christopher Summerfield explores these questions, charting AI's evolution from early ideas in the seventeenth century to today's deep neural networks. His book is the most accessible, up-to-date, and authoritative exploration of this radical technology. With an understanding of AI's inner workings, we can address the existential question of our age: can we look forward to a technological utopia, or are we writing ourselves out of history? 'As a leading authority...Summerfield is perfectly situated to explore the meaning and implications of these machines that are so uncannily like - and unlike - ourselves' Brian Christian, co-author of Algorithms to Live by 'You might choose to be alarmed, excited, or indifferent to LLMs, but you should read Chris's book before you decide' Mike Woolridge, author of The Road to Conscious Machines

How AI Learned to Talk and What It Means

These Strange New Minds

Rapidly transform your analytics teams to deliver AI-driven insights.

The AI-driven Data Team by Nicholas Kelly is a proven-to-work playbook for data and analytics leaders who want to align analytics capabilities with the demands of an AI-powered business environment. Written for leaders accountable for data, analytics and business intelligence, this book provides tools for diagnosing capability gaps among data analytics teams, modernizing legacy stacks and delivering AI-driven insights to help organizations make better decisions. 

You'll learn how to:
- Diagnose skills gaps and map AI-augmented career paths
- Integrate modern AI tools with existing analytics stacks
- Launch six revenue-driving and cost-focused pilots in 90 days
- Embed ISO and NIST-aligned governance without slowing innovation
- Apply ROI calculators, governance checklists and sprint planners

Drawing on expert insights and real-world applications, this book helps you upskill your analysts, strengthen AI governance and provide AI-driven insights that drive real results.

Themes include: AI strategy, data governance, analytics leadership, ROI from AI, organizational transformation, executive decision-making

Improve Your Analytics with AI

The Ai-Driven Data Team

Data Engineering Design Patterns

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