Implementing Data Mesh
As data continues to grow and become more complex, organizations seek innovative solutions to manage their data effectively. Data mesh is one solution that provides a new approach to managing data in complex organizations. This practical guide offers step-by-step guidance on how to implement data mesh in your organization. In this book, Jean-Georges Perrin and Eric Broda focus on the key components of data mesh and provide practical advice supported by code. Data engineers, architects, and analysts will explore a simple and intuitive process for identifying key data mesh components and data products. You'll learn a consistent set of interfaces and access methods that make data products easy to consume. This approach ensures that your data products are easily accessible and the data mesh ecosystem is easy to navigate. This book helps you: Identify, define, and build data products that interoperate within an enterprise data mesh Build a data mesh fabric that binds data products together Build and deploy data products in a data mesh Establish the organizational structure to operate data products, data platforms, and data fabric Learn an innovative architecture that brings data products and data fabric together into the data mesh About the authors: Jean-Georges "JG" Perrin is a technology leader focusing on building innovative and modern data platforms. Eric Broda is a technology executive, practitioner, and founder of a boutique consulting firm that helps global enterprises realize value from data.
Llms in Production
Learn how to put Large Language Model-based applications into production safely and efficiently. Large Language Models (LLMs) are the foundation of AI tools like ChatGPT, LLAMA and Bard. This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. In LLMs in Production you will: Grasp the fundamentals of LLMs and the technology behind them Evaluate when to use a premade LLM and when to build your own Efficiently scale up an ML platform to handle the needs of LLMs Train LLM foundation models and finetune an existing LLM Deploy LLMs to the cloud and edge devices using complex architectures like RLHF Build applications leveraging the strengths of LLMs while mitigating their weaknesses LLMs in Production delivers vital insights into delivering MLOps for LLMs. You'll learn how to operationalize these powerful AI models for chatbots, coding assistants, and more. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice.
Domains and Types in Relational Theory and SQL
What's a domain? What's a type? The original version of the relational model (defined by Codd in 1969) had domains and not types. The original version of the SQL standard (defined by committee in 1986, but based on work done by Boyce and Chamberlin et al. in the 1970s) had types and not domains. Later, Codd added types and SQL added domains. But no two of these constructs-domains per Codd, types per Codd, domains per SQL, types per SQL-are the same thing! Other researchers, designers, and implementers then got into the act with further definitions of their own, and the picture became, and remains, more confused than ever. This book is an attempt to clear the air. To be specific, it argues that: Domains per Codd aren't really domains in the mathematical sense but are much closer to types in the computing sense, and types per Codd don't belong in the model at all. Types per SQL are really several different concepts all mixed together in confusing ways, and domains per SQL are just a red herring. Properly understood, domains and types are the same thing after all. The book justifies these claims by describing and analyzing the pertinent aspects of both SQL and the relational model in detail.
AI & Machine Learning in Education
Discover the exciting world of AI and Machine Learning in Education.Explore the history, applications, and ethical considerations.Learn how to build intelligent learning environments and address challenges.Get insights from case studies and best practices.Prepare for the future with AI-driven education.
Data Engineering Design Patterns
Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies are still trying to solve problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more. Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios. Throughout this journey, you'll use open source data tools and public cloud services to see how to put each pattern into practice. You'll learn: Challenges data engineers face and their impact on data systems How these challenges relate to data system components What data engineering patterns are for How to identify and fix issues with your current data components Technology-agnostic solutions to new and existing data projects How to implement patterns with Apache Airflow, Apache Spark, Apache Flink, and Delta Lake Bartosz Konieczny is a freelance data engineer who's been coding for more than 15 years. He's held various senior hands-on positions that helped him work on many data engineering problems in batch and stream processing.
Building Intelligent Applications with Generative AI
DESCRIPTION Building Intelligent Applications with Generative AI is a comprehensive guide that unlocks the power of generative AI for building cutting-edge applications. This book covers a wide range of use cases and practical examples, from text generation and conversational agents to creative media generation and code completion. These examples are designed to help you capitalize on the potential of generative AI in your applications.Through clear explanations, step-by-step tutorials, and real-world case studies, you will learn how to prepare data and train generative AI models. You will also explore different generative AI techniques, including large language models like GPT-4, ChatGPT, Llama 2, and Google's Gemini, to understand how they can be applied in various domains, such as content generation, virtual assistants, and code generation. With a focus on practical implementation, this book also examines ethical considerations, best practices, and future trends in generative AI. Further, this book concludes by exploring ethical considerations and best practices for building responsible GAI applications, ensuring you are harnessing this technology for good. By the end of this book, you will be well-equipped to leverage the power of GAI to build intelligent applications and unleash your creativity in innovative ways.WHAT YOU WILL LEARN● Understand generative AI (GAI) and large language models (LLMs).● Explore real-world GAI applications across industries.● Build intelligent applications with the ChatGPT API.● Explore retrieval augmented generation with LangChain and Gemini Pro.● Create chatbots with LangChain and Streamlit for data retrieval.WHO THIS BOOK IS FORThis book is for developers, data scientists, AI practitioners, and tech enthusiasts who are interested in leveraging generative AI techniques to build intelligent applications across various domains.
Advances in Artificial Intelligence and Machine Learning in Big Data Processing
This book constitutes the refereed proceedings of the First International Conference on Advances in Artificial Intelligence & Machine Learning in Big Data Processing, AAIMB 2023, held in Chennai, India, during August 17-18, 2023. The 51 full papers presented were carefully reviewed and selected from 183 submissions. They were organized in the following topical sections: Part I- artificial intelligence and data analytics; deep learning. Part II- artificial intelligence and data analytics; machine learning.
Advances in Artificial Intelligence and Machine Learning in Big Data Processing
This book constitutes the refereed proceedings of the First International Conference on Advances in Artificial Intelligence & Machine Learning in Big Data Processing, AAIMB 2023, held in Chennai, India, during August 17-18, 2023. The 51 full papers presented were carefully reviewed and selected from 183 submissions. They were organized in the following topical sections: Part I- artificial intelligence and data analytics; deep learning. Part II- artificial intelligence and data analytics; machine learning.
Oracle Database 23ai Data Modeling and Schema Design for JSON-Relational Duality
The Align > Refine > Design series covers conceptual, logical, and physical data modeling (schema design and patterns) for leading technologies, combining proven data modeling practices with database-specific features to produce better applications. JSON-Relational Duality is a new concept that combines aspects of relational databases and document databases in one combined data model. Read Oracle Database 23ai Data Modeling and Schema Design for JSON-Relational Duality if you are a data professional who needs to expand your modeling skills to include Oracle 23ai JSON-Relational Duality or a technologist who works with either relational or document databases and want to learn how to benefit from combining both models.We cover the three modeling characteristics of precise, minimal, and visual; the three model components of entities, relationships, and attributes (including keys); the three model levels of conceptual (align), logical (refine), and physical (design); and the three modeling perspectives of relational, dimensional, and query. Align is about agreeing on the common business vocabulary so everyone is aligned on terminology and general initiative scope. Refine is about capturing the business requirements. That is, refining our knowledge of the initiative to focus on what is essential. Design is about the technical requirements. That is, designing to accommodate our model's unique software and hardware needs.If you are interested in learning how to build multiple database solutions, read all the books in the Align > Refine > Design series. Since each book uses the same template, you can quickly skill up on additional database technologies.
Python Feature Engineering Cookbook - Third Edition
Leverage the power of Python to build real-world feature engineering and machine learning pipelines ready to be deployed to productionKey Features: - Craft powerful features from tabular, transactional, and time-series data- Develop efficient and reproducible real-world feature engineering pipelines- Optimize data transformation and save valuable time- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Streamline data preprocessing and feature engineering in your machine learning project with this third edition of the Python Feature Engineering Cookbook to make your data preparation more efficient.This guide addresses common challenges, such as imputing missing values and encoding categorical variables using practical solutions and open source Python libraries.You'll learn advanced techniques for transforming numerical variables, discretizing variables, and dealing with outliers. Each chapter offers step-by-step instructions and real-world examples, helping you understand when and how to apply various transformations for well-prepared data.The book explores feature extraction from complex data types such as dates, times, and text. You'll see how to create new features through mathematical operations and decision trees and use advanced tools like Featuretools and tsfresh to extract features from relational data and time series.By the end, you'll be ready to build reproducible feature engineering pipelines that can be easily deployed into production, optimizing data preprocessing workflows and enhancing machine learning model performance.What You Will Learn: - Discover multiple methods to impute missing data effectively- Encode categorical variables while tackling high cardinality- Find out how to properly transform, discretize, and scale your variables- Automate feature extraction from date and time data- Combine variables strategically to create new and powerful features- Extract features from transactional data and time series- Learn methods to extract meaningful features from text dataWho this book is for: If you're a machine learning or data science enthusiast who wants to learn more about feature engineering, data preprocessing, and how to optimize these tasks, this book is for you. If you already know the basics of feature engineering and are looking to learn more advanced methods to craft powerful features, this book will help you. You should have basic knowledge of Python programming and machine learning to get started.Table of Contents- Imputing Missing Data- Encoding Categorical Variables- Transforming Numerical Variables- Performing Variable Discretization- Working with Outliers- Extracting Features from Date and Time Variables- Performing Feature Scaling- Creating New Features- Extracting Features from Relational Data with Featuretools- Creating Features from a Time Series with tsfresh- Extracting Features from Text Variables
Relational and Algebraic Methods in Computer Science
This book constitutes the refereed proceedings of the 21st International Conference, RAMiCS 2024, held in Prague, Czech Republic, during August 19-22, 2024. The 15 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on mathematical foundations to applications as conceptual and methodological tools in computer science and beyond.
The Art of Prompt Engineering
Unlocking the Future: Innovations in Tech and Education.Discover the journey of Alexandru Cocindau, a visionary entrepreneur and esteemed author with more than 19 years of experience in software development, mobile app innovation, e-commerce, gaming and more. As the founder of TeachMeCode Institute, Cocindau has dedicated his career to educating the next generation of tech leaders.In this insightful book, Cocindau delves into the artificial intelligence transformative power over various domains such as defense, agriculture, healthcare, and fintech. He shares his expertise on automation, AI, and the critical importance of mastering prompt engineering."Mastering AI and prompt engineering is key to future self education," says Cocindau, offering readers a roadmap to stay ahead in an ever-evolving tech landscape. Join Alexandru Cocindau on a journey through groundbreaking advancements and learn how to harness the power of technology to shape a brighter future.
Database Design and Modeling with PostgreSQL and MySQL
Become well-versed with database modeling and SQL optimization, and gain a deep understanding of transactional systems through practical examples and exercisesKey Features: - Get to grips with fundamental-to-advanced database design and modeling concepts with PostgreSQL and MySQL- Explore database integration with web apps, emerging trends, and real-world case studies- Leverage practical examples and hands-on exercises to reinforce learning- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Database Design and Modeling with PostgreSQL and MySQL will equip you with the knowledge and skills you need to architect, build, and optimize efficient databases using two of the most popular open-source platforms.As you progress through the chapters, you'll gain a deep understanding of data modeling, normalization, and query optimization, supported by hands-on exercises and real-world case studies that will reinforce your learning. You'll explore topics like concurrency control, backup and recovery strategies, and seamless integration with web and mobile applications. These advanced topics will empower you to tackle complex database challenges confidently and effectively. Additionally, you'll explore emerging trends, such as NoSQL databases and cloud-based solutions, ensuring you're well-versed in the latest developments shaping the database landscape. By embracing these cutting-edge technologies, you'll be prepared to adapt and innovate in today's ever-evolving digital world.By the end of this book, you'll be able to understand the technologies that exist to design a modern and scalable database for developing web applications using MySQL and PostgreSQL open-source databases.What You Will Learn: - Design a schema, create ERDs, and apply normalization techniques- Gain knowledge of installing, configuring, and managing MySQL and PostgreSQL- Explore topics such as denormalization, index optimization, transaction management, and concurrency control- Scale databases with sharding, replication, and load balancing, as well as implement backup and recovery strategies- Integrate databases with web apps, use SQL, and implement best practices- Explore emerging trends, including NoSQL databases and cloud databases, while understanding the impact of AI and MLWho this book is for: This book is for a wide range of professionals interested in expanding their knowledge and skills in database design and modeling with PostgreSQL and MySQL. This includes software developers, database administrators, data analysts, IT professionals, and students. While prior knowledge of MySQL and PostgreSQL is not necessary, some familiarity with at least one relational database management system (RDBMS) will help you get the most out of this book.Table of Contents- SQL and NoSQL Databases: Characteristics, Design, and Trade-Offs- Building a Strong Foundation for Database Design- Getting Your Hands Dirty with PostgreSQL and MySQL- Mastering the Building Blocks of Database Design and Modeling- Advanced Techniques for Advanced Databases- Understanding Database Scalability- Best Practices for Building and Maintaining Your Database- The Future of Databases and Their Designs
Predictive Data Modelling for Biomedical Data and Imaging
This book explores predictive data modeling for biomedical data and imaging in healthcare.
Database Technology
Essentially all truly general purpose database systems today are based on the relational model of data. That model was invented by E. F. ("Ted") Codd in the late 1960s and early 1970s, during his employment at the IBM Research Laboratory in San Jose, California, and it was solidly based on conventional two-valued logic, 2VL. Unfortunately, those same general purpose database systems today all use the language SQL as their user language-and SQL is based not on 2VL but on what's called three-valued logic, 3VL, instead, and uses what are called nulls to represent missing information. In fact, Codd himself subsequently extended his original model to support 3VL and nulls as well. (Actually, basing SQL on 3VL and adding 3VL to the relational model seem to have occurred in lockstep, as it were. Certainly both developments occurred at the same place-the IBM San Jose Research Laboratory-at more or less the same time.) But 3VL is a disastrously bad basis on which to build databases, or database applications, or database languages.It's bad because 3VL doesn't behave the way reality behaves, which means that results that 3VL says are correct are often not correct in the real world. Not only that, but SQL's implementation of 3VL is itself incorrect in places, which means it effectively adds another layer of wrong answers on top of the first. Bottom line: If you have nulls in your database, you're getting wrong answers. This book explains this issue in detail. It also contains numerous practical suggestions for dealing with the problem.
AI Apocalypse
In AI Apocalypse: How Robots Will Rule the World by Elon Muskrat, explore the history, impact, and ethical considerations of artificial intelligence. From the rise of AI to its potential in healthcare, education, and transportation, this book delves into the benefits and consequences of robotics and automation. Fear not, for it also highlights AI's potential for good and the importance of ethical guidelines. With a focus on the future of AI and the challenges it presents, this book calls for action from policymakers and technologists. The time to understand and shape the future is now.
Big Data on Kubernetes
Gain hands-on experience in building efficient and scalable big data architecture on Kubernetes, utilizing leading technologies such as Spark, Airflow, Kafka, and TrinoKey Features: - Leverage Kubernetes in a cloud environment to integrate seamlessly with a variety of tools- Explore best practices for optimizing the performance of big data pipelines- Build end-to-end data pipelines and discover real-world use cases using popular tools like Spark, Airflow, and Kafka- Purchase of the print or Kindle book includes a free PDF eBookBook Description: In today's data-driven world, organizations across different sectors need scalable and efficient solutions for processing large volumes of data. Kubernetes offers an open-source and cost-effective platform for deploying and managing big data tools and workloads, ensuring optimal resource utilization and minimizing operational overhead. If you want to master the art of building and deploying big data solutions using Kubernetes, then this book is for you.Written by an experienced data specialist, Big Data on Kubernetes takes you through the entire process of developing scalable and resilient data pipelines, with a focus on practical implementation. Starting with the basics, you'll progress toward learning how to install Docker and run your first containerized applications. You'll then explore Kubernetes architecture and understand its core components. This knowledge will pave the way for exploring a variety of essential tools for big data processing such as Apache Spark and Apache Airflow. You'll also learn how to install and configure these tools on Kubernetes clusters. Throughout the book, you'll gain hands-on experience building a complete big data stack on Kubernetes.By the end of this Kubernetes book, you'll be equipped with the skills and knowledge you need to tackle real-world big data challenges with confidence.What You Will Learn: - Install and use Docker to run containers and build concise images- Gain a deep understanding of Kubernetes architecture and its components- Deploy and manage Kubernetes clusters on different cloud platforms- Implement and manage data pipelines using Apache Spark and Apache Airflow- Deploy and configure Apache Kafka for real-time data ingestion and processing- Build and orchestrate a complete big data pipeline using open-source tools- Deploy Generative AI applications on a Kubernetes-based architectureWho this book is for: If you're a data engineer, BI analyst, data team leader, data architect, or tech manager with a basic understanding of big data technologies, then this big data book is for you. Familiarity with the basics of Python programming, SQL queries, and YAML is required to understand the topics discussed in this book. Table of Contents- Getting Started with Containers- Kubernetes Architecture- Kubernetes - Hands On- The Modern Data Stack - Big Data processing with Apache Spark- Apache Airflow for building pipelines- Apache Kafka for real time events and data ingestion- Deploying the Big Data Stack on Kubernetes- Data consumption layer- Building a Big Data Pipeline on Kubernetes- AI/ML Workloads on Kubernetes- Where to go from here
Data Management Strategy at Microsoft
Leverage your data as a business asset, from readiness to actionable insights, and drive exceptional performanceKey Features: - Learn strategies to create a data-driven culture and align data initiatives with business goals- Navigate the ever-evolving business landscape with a modern data platform and unique Data IP- Surpass competitors by harnessing the true value of data and fostering data literacy in your organization- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Microsoft pioneered data innovation and investment ahead of many in the industry, setting a remarkable standard for data maturity. Written by a data leader with over 15 years of experience following Microsoft's data journey, this book delves into every crucial aspect of this journey, including change management, aligning with business needs, enhancing data value, and cultivating a data-driven culture.This book emphasizes that success in a data-driven enterprise goes beyond relying solely on modern technology and highlights the importance of prioritizing genuine business needs to propel necessary modernizations through change management practices. You'll see how data-driven innovation does not solely reside within central IT engineering teams but also among the data's business owners who rely on data daily for their operational needs. This guide empower these professionals with clean, easily discoverable, and business-ready data, marking a significant breakthrough in how data is perceived and utilized throughout an enterprise. You'll also discover advanced techniques to nurture the value of data as unique intellectual property, and differentiate your organization with the power of data.Its storytelling approach and summary of essential insights at the end of each chapter make this book invaluable for business and data leaders to advocate for crucial data investments.What You Will Learn: - Develop a data-driven roadmap to achieve significant and quantifiable business goals- Discover the ties between data management and change management- Explore the data maturity curve with essential technology investments- Build, safeguard, and amplify your organization's unique Data Intellectual Property- Equip business leaders with trustworthy and high value data for informed decision-making- Unleash the value of data management and data governance to uplift your data investmentsWho this book is for: This book is for data leaders, CDOs, CDAOs, data practitioners, data stewards, and enthusiasts, as well as modern business leaders intrigued by the transformative potential of data. While a technical background isn't essential, a basic understanding of data management and quality concepts will be helpful. The book avoids twisted technical, engineering, or data science aspects, making it accessible and insightful for data engineers and data scientists to gain a wider understanding of enterprise data needs and challenges.Table of Contents- Where's My Data and Who's in Charge?- We Make Data Business Ready- Thousands to One - From Locally Siloed to Globally Centralized- Reactive! Proactive? Predictive- Mastering Your Data Domains and Business Ownership- Navigating the Strategic Data Dilemma- Unique Data IP Is Your Magic- The Pareto Principle in Action- Data Mastering and MDM - Data Mesh and Data Governance- Data Assets or Data Products?- Data Value, Literacy, and Culture- Getting Ready for GenAI
Real-World Edge Computing
Learn how to use Linux Foundation's LF Edge Open Horizon project to scale the deployment of your edge application and workload solution in a production environmentKey Features: - Explore historical trends, industries, use cases, and the evolving ecosystem of edge computing- Build your skills to recognize common issues that can derail a successful edge project- Understand the symbiotic relationship between edge computing and application-centric networking- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Edge computing holds vast potential to revolutionize industries, yet its implementation poses unique challenges. Written by industry veterans Rob High and Sanjeev Gupta, this comprehensive guide bridges the gap between theory and practice. Distilling expertise from their combined decades of experience in edge computing and hybrid cloud mesh solutions, this book equips software developers and DevOps teams with the knowledge and skills needed to deploy edge solutions at scale in production environments. It also explores foundational standards and introduces key factors that may impede the scaling of edge solutions.While edge computing draws from the successes of cloud computing, crucial distinctions separate the two. High and Gupta elucidate these distinctions, helping you grasp the nuanced dynamics of edge-computing ecosystems. With a focus on leveraging Open Horizon to overcome pitfalls and optimize performance, this book will help you confidently navigate the intricacies of constructing and deploying resilient edge solutions in real-world production settings.By the end of this book, you'll have acquired a deep understanding of essential success factors for building and deploying robust edge solutions in real-world production settings, leveraging Open Horizon for scalable edge deployments.What You Will Learn: - Identify prevalent challenges in edge projects- Use Open Horizon to address project issues- Optimize Open Horizon functionality effectively- Debug and resolve field issues with precision- Deploy AI/ML models on edge devices seamlessly- Enhance team productivity in Edge project management- Implement best practices for organizing all your edge projectsWho this book is for: This book is for software engineers and DevOps teams aiming to optimize their edge solutions. Whether you're already developing edge solutions or in the planning stages, this book equips you with insights to mitigate risks and enhance viability. Catering to various experience levels, it offers valuable guidance on understanding the nuances between cloud and edge computing.Table of Contents- Fundamentals of Edge Computing- Practicalities of Edge Computing- Designing and Building Edge Software- Edge Container Platforms- Application Placement and Lifecycle Management- Installing an Edge Agent on an Edge Node- Registering an Edge Node- Using the Open Horizon CLI and API- Autonomous and Flexible Management of Edge Services- Managing Edge Scale with Policy-Based Workload Placement- Machine Learning Workload and Model Deployment- Security at the Edge- Network Connectivity at the Edge- Building a Real-World Example Application- Troubleshooting at the Edge- Follow-On Topics- Using the IBM Edge Application Manager Web UI
Responsible Artificial Intelligence Re-Engineering the Global Public Health Ecosystem
Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem: A Humanity Worth Saving is the first comprehensive book showing how trustworthy AI can revolutionize decolonized global public health. It explains how it works as an ecosystem and how it can be fixed to equitably empower us all to solve the defining crises of our era, from poverty to pandemics, climate to conflicts, debt to divisions. It is written from the first-hand perspective of the world's first triple doctorate trained physician-data scientist and ethicist who has cared for more than 10,000 patients and authored 5 AI textbooks and more than 400 scientific and ethics papers. This essential resource integrates science, political economics, and ethics to unite our unique cultures, belief systems, institutions, and governments. In doing so, it is meant to give humanity a fighting chance against shared existential threats through cooperation and managed strategic competition for integral sustainable development. Taking seriously diverse voices, perspectives, and insights from the Global North and the Global South, this book uses concrete examples backed up by clear explanations to elucidate the current failures, emerging successes, and societal trends of global public health. It shows how a small number of powerful governments and corporations--amid digitalization, deglobalization, and demographic shifts--dominate global health, and how we can re-engineer a better future for it both societally and technologically. The book spans health breakthroughs in federated data architectures, machine learning, deep learning, swarm learning, quantum computing, blockchain, agile data governance and solidarity, value blocks (of democracies and autocracies), adaptive value supply chains, social networks, pandemics, health financing, universal health coverage, public-private partnerships, healthcare system design, precision agriculture, clean energy, human security, and multicultural global ethics. This book therefore is meant to provide a clear, coherent, and actionable guide equipping students, practitioners, researchers, policymakers, and leaders in digital technology, public health, healthcare, health policy, public policy, political economics, and ethics to generate the solutions that will define humanity's next era--while recovering what that humanity means, and why it is worth saving.
Ultimate Pandas for Data Manipulation and Visualization
Unlock the power of Data Manipulation with Pandas. Book DescriptionUnlock the power of Pandas, the essential Python library for data analysis and manipulation. This comprehensive guide takes you from the basics to advanced techniques, ensuring you master every aspect of pandas. You'll start with an introduction to pandas and data analysis, followed by in-depth explorations of pandas Series and DataFrame, the core data structures. Learn essential skills for data cleaning and filtering, and master grouping and aggregation techniques to summarize and analyze your data sets effectively. Discover how to reshape and pivot data, join and merge multiple datasets, and handle time series analysis. Enhance your data analysis with compelling visualizations using Matplotlib, and apply your knowledge in a real-world scenario by analyzing bank customer churn. Through hands-on examples and practical use cases, this book equips you with the tools to clean, filter, aggregate, reshape, merge, and visualize data effectively, transforming it into actionable insights. Table of Contents1. Introduction to Pandas and Data Analysis2. Pandas Series3. Pandas DataFrame4. Data Cleaning with Pandas5. Data Filtering with Pandas6. Grouping and Aggregating Data7. Reshaping and Pivoting in Pandas8. Joining and Merging Data in Pandas9. Introduction to Time Series Analysis in Pandas10. Visualization Using Matplotlib11. Analyzing Bank Customer Churn Using Pandas Index
NGINX HTTP Server - Fifth Edition
Master NGINX to elevate, accelerate, and optimize your cloud infrastructureKey Features- Discover the potential of running NGINX in your environment- Run NGINX in harmony with your existing cloud infrastructure- Learn how to fine-tune and adjust NGINX's configuration for best performance- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionExplore the capabilities of NGINX, a robust HTTP server designed for handling high-traffic websites, with network scalability as its primary objective. Whether you're a beginner or an experienced administrator, this NGINX book will guide you through the complete process of setting up this lightweight HTTP server, from quick and basic configurations to more detailed configurations tailored to your needs. Highlighting the latest version 1.25.2, featuring new features such as HTTP/3 and QUIC, this edition keeps you up to date with cutting-edge developments.This book is packed with a multitude of real-world examples, which will help you secure your infrastructure with automatic TLS certificates, expertly place NGINX in front of your existing applications, and do much more. From orchestration and Docker to bandwidth management, OpenResty, and NGINX Plus commercial features, you'll get to grips with enhancing and optimizing your infrastructure or designing brand-new architecture. Moreover, this updated edition will show you how NGINX excels in cloud environments with guides on integrating NGINX with cloud services for deploying scalable architectures efficiently and securely.By the end of this book, you'll be able to adapt and use a wide variety of NGINX implementations to tackle diverse challenges with confidence.What you will learn- Install and configure a basic setup for NGINX and test it out- Discover the core functionality of the HTTP module as well as third-party modules- Understand how to set up NGINX to work with PHP, Python, and other applications- Optimize your architecture with threads or load balancing- Configure NGINX with orchestration and work in a Docker environment- Identify errors in configuration and grasp basic troubleshooting techniquesWho this book is forThis book is for beginners and web administrators looking to master the powerful and secure NGINX HTTP server. Whether you want to replace your existing web server software or integrate a new tool to collaborate with applications that are already up and running, this book will help you achieve your goals. To get started, all you need is access to a Linux server and a basic understanding of web server concepts.Table of Contents- Downloading and Installing NGINX- Basic NGINX Configuration- Exploring the HTTP Configuration- Exploring Module Configuration in NGINX- PHP and Python with NGINX- NGINX as a Reverse Proxy- Introduction to Load Balancing and Optimization- NGINX within a Cloud Infrastructure- Fully Deploy, Manage, and Auto-Update NGINX with Ansible- Case Studies- Troubleshooting
Data Engineering with Databricks Cookbook
Work through 70 recipes for implementing reliable data pipelines with Apache Spark, optimally store and process structured and unstructured data in Delta Lake, and use Databricks to orchestrate and govern your dataKey Features: - Learn data ingestion, data transformation, and data management techniques using Apache Spark and Delta Lake- Gain practical guidance on using Delta Lake tables and orchestrating data pipelines- Implement reliable DataOps and DevOps practices, and enforce data governance policies on Databricks- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Written by a Senior Solutions Architect at Databricks, Data Engineering with Databricks Cookbook will show you how to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, starting with comprehensive introduction to data ingestion and loading with Apache Spark.What makes this book unique is its recipe-based approach, which will help you put your knowledge to use straight away and tackle common problems. You'll be introduced to various data manipulation and data transformation solutions that can be applied to data, find out how to manage and optimize Delta tables, and get to grips with ingesting and processing streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Advanced recipes later in the book will teach you how to use Databricks to implement DataOps and DevOps practices, as well as how to orchestrate and schedule data pipelines using Databricks Workflows. You'll also go through the full process of setup and configuration of the Unity Catalog for data governance.By the end of this book, you'll be well-versed in building reliable and scalable data pipelines using modern data engineering technologies.What You Will Learn: - Perform data loading, ingestion, and processing with Apache Spark- Discover data transformation techniques and custom user-defined functions (UDFs) in Apache Spark- Manage and optimize Delta tables with Apache Spark and Delta Lake APIs- Use Spark Structured Streaming for real-time data processing- Optimize Apache Spark application and Delta table query performance- Implement DataOps and DevOps practices on Databricks- Orchestrate data pipelines with Delta Live Tables and Databricks Workflows- Implement data governance policies with Unity CatalogWho this book is for: This book is for data engineers, data scientists, and data practitioners who want to learn how to build efficient and scalable data pipelines using Apache Spark, Delta Lake, and Databricks. To get the most out of this book, you should have basic knowledge of data architecture, SQL, and Python programming.Table of Contents- Data Ingestion and Data Extraction with Apache Spark- Data Transformation and Data Manipulation with Apache Spark- Data Management with Delta Lake- Ingesting Streaming Data- Processing Streaming Data- Performance Tuning with Apache Spark- Performance Tuning in Delta Lake- Orchestration and Scheduling Data Pipeline with Databricks Workflows- Building Data Pipelines with Delta Live Tables- Data Governance with Unity Catalog- Implementing DataOps and DevOps on Databricks
Hands-On Differential Privacy
Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help. Authors Ethan Cowan, Michael Shoemate, and Mayana Pereira explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and understand how to use open source tools to create differentially private statistics, explore how to assess the utility/privacy trade-offs, and learn how to integrate differential privacy into workflows. With this book, you'll learn: How DP guarantees privacy when other data anonymization methods don't What preserving individual privacy in a dataset entails How to apply DP in several real-world scenarios and datasets Potential privacy attack methods, including what it means to perform a reidentification attack How to use the OpenDP library in privacy-preserving data releases How to interpret guarantees provided by specific DP data releases
The Ultimate Guide to Snowpark
Develop robust data pipelines, deploy mature machine learning models, and build secure data apps with Snowflake Snowpark using PythonKey Features: - Get to grips with Snowflake Snowpark's basic and advanced features- Implement workloads in domains like data engineering, data science, and data applications using Snowpark with Python- Deploy Snowpark in production with practical examples and best practices- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Snowpark is a powerful framework that helps you unlock numerous possibilities within the Snowflake Data Cloud. However, without proper guidance, leveraging the full potential of Snowpark with Python can be challenging. Packed with practical examples and code snippets, this book will be your go-to guide to using Snowpark with Python successfully.The Ultimate Guide to Snowpark helps you develop an understanding of Snowflake Snowpark and how it enables you to implement workloads in data engineering, data science, and data applications within the Data Cloud. From configuration and coding styles to workloads such as data manipulation, collection, preparation, transformation, aggregation, and analysis, this guide will equip you with the right knowledge to make the most of this framework. You'll discover how to build, test, and deploy data pipelines and data science models. As you progress, you'll deploy data applications natively in Snowflake and operate large language models (LLMs) using Snowpark container services.By the end of this book, you'll be able to leverage Snowpark's capabilities and propel your career as a Snowflake developer to new heights.What You Will Learn: - Harness Snowpark with Python for diverse workloads- Develop robust data pipelines with Snowpark using Python- Deploy mature machine learning models- Explore the process of developing, deploying, and monetizing native apps using Snowpark- Deploy and operate containers in Snowpark- Discover the pathway to adopting Snowpark effectively in productionWho this book is for: This book is for data engineers, data scientists, developers, and data practitioners seeking an in-depth understanding of Snowpark's features and best practices for deploying various workloads in Snowpark using the Python programming language. Basic knowledge of SQL, proficiency in Python, an understanding of data engineering and data science basics, and familiarity with the Snowflake Data Cloud platform are required to get the most out of this book.Table of Contents- Discovering Snowpark- Establishing Foundation with Snowpark- Simplifying Data Processing using Snowpark- Building Data Engineering Pipelines with Snowpark- Developing Data Science Projects with Snowpark- Deploying and managing ML models with Snowpark- Developing Native application with Snowpark- Introduction to Snowpark Container Services
Just the Facts
Build more successful databases and applications through improved business and data understanding using Fact-oriented Modeling. Welcome to the intriguing realm where data seems to be everywhere we look. It runs in apps, drives businesses, and shapes public opinions and politics. As data affects our daily lives, we must pay more attention to what the data is about and how we communicate as human beings.It is not the data itself, but our communication about the data that builds bridges of understanding. This book aims to bring a holistic view to three distinctive audiences, enabling all to communicate about data and collaborate more effectively. Thanks to our human capability to communicate, we can build bridges that connect people, transcend silos, and align technology in a way that propels organizations forward into this digital age.Whether you are a business visionary, tech enthusiast, or someone navigating the ever-changing landscape of data, this book is your roadmap to a future where communication, technology, and business harmoniously coexist. As you embark on this journey, you'll find that the book caters to three audiences: Business Employees and Domain Experts. The first section brings the business into focus. We address the needs of business employees and domain experts at the forefront of operations and strategy. We explore how they can harness the power of communication to contribute to the success of the organization.Business Information and Data Modelers. The second section delves into the realm of information modeling. Here, we cater to the audience whose expertise lies in capturing and documenting business information and ensure it lays out the guidelines to help IT provide products and services that meet business requirements.Developers and Engineers. The third section focuses on the developers and engineers who form the backbone of technology implementation. It dives deeper into the intricate world of data, transforming raw data into valuable information, insights, and applications.In collaborative environments, individuals share information about their experiences. Human history began with the oral transmission of facts, progressed with the advent of the printing press, and has now reached a digital era where computers can store and process this information. Despite these advancements, the fundamental purpose of communication remains unchanged: the dissemination of information.It's not just about the data, data model, or information model. The act of modeling serves to communicate the needs of the organization. The act of information modeling allows us all to engage with each other using the fact-based modeling method you will apply through the many examples within these pages.
Apache Iceberg: The Definitive Guide
Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool--a cost-prohibitive process for making warehouse features available to all of your data. The lack of flexibility with these patterns requires you to lock into a set of priority tools and formats, which creates data silos and data drift. This practical book shows you a better way. Apache Iceberg provides the capabilities, performance, scalability, and savings that fulfill the promise of an open data lakehouse. By following the lessons in this book, you'll be able to achieve interactive, batch, machine learning, and streaming analytics with this high-performance open source format. Authors Tomer Shiran, Jason Hughes, and Alex Merced from Dremio show you how to get started with Iceberg. With this book, you'll learn: The architecture of Apache Iceberg tables What happens under the hood when you perform operations on Iceberg tables How to further optimize Iceberg tables for maximum performance How to use Iceberg with popular data engines such as Apache Spark, Apache Flink, and Dremio Discover why Apache Iceberg is a foundational technology for implementing an open data lakehouse.
Databricks ML in Action
Get to grips with autogenerating code, deploying ML algorithms, and leveraging various ML lifecycle features on the Databricks Platform, guided by best practices and reusable code for you to try, alter, and build onKey Features: - Build machine learning solutions faster than peers only using documentation- Enhance or refine your expertise with tribal knowledge and concise explanations- Follow along with code projects provided in GitHub to accelerate your projects- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Discover what makes the Databricks Data Intelligence Platform the go-to choice for top-tier machine learning solutions. Written by a team of industry experts at Databricks with decades of combined experience in big data, machine learning, and data science, Databricks ML in Action presents cloud-agnostic, end-to-end examples with hands-on illustrations of executing data science, machine learning, and generative AI projects on the Databricks Platform.You'll develop expertise in Databricks' managed MLflow, Vector Search, AutoML, Unity Catalog, and Model Serving as you learn to apply them practically in everyday workflows. This Databricks book not only offers detailed code explanations but also facilitates seamless code importation for practical use. You'll discover how to leverage the open-source Databricks platform to enhance learning, boost skills, and elevate productivity with supplemental resources.By the end of this book, you'll have mastered the use of Databricks for data science, machine learning, and generative AI, enabling you to deliver outstanding data products.What You Will Learn: - Set up a workspace for a data team planning to perform data science- Monitor data quality and detect drift- Use autogenerated code for ML modeling and data exploration- Operationalize ML with feature engineering client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, and Workflows- Integrate open-source and third-party applications, such as OpenAI's ChatGPT, into your AI projects- Communicate insights through Databricks SQL dashboards and Delta Sharing- Explore data and models through the Databricks marketplaceWho this book is for: This book is for machine learning engineers, data scientists, and technical managers seeking hands-on expertise in implementing and leveraging the Databricks Data Intelligence Platform and its Lakehouse architecture to create data products.Table of Contents- Getting Started with This Book and Lakehouse Concepts- Designing Databricks: Day One- Building Out Our Bronze Layer- Getting to Know Your Data- Feature Engineering on Databricks- Searching for a Signal- Productionizing ML on Databricks- Monitoring, Evaluating, and More
Business Intelligence with Looker Cookbook
Use Looker for visualizing data, data analysis, and reporting, and learn how to connect to your data, build dashboards and reports, and share insights with your teamKey FeaturesExplore data visualization, analysis, and reporting with Looker to gain insights from your dataConnect to data sources, build dashboards, and create reports to track and share key metricsShare insights with your team to make better business decisionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionLooker is a data analytics and business intelligence platform that allows organizations to explore, analyze, and visualize their data. It provides tools for data modeling, exploration, and visualization, enabling you to gain insights from your data to make informed business decisions.You'll start with the basics, from setting up your Looker environments to configuring views and models using LookML. As you progress, you'll delve into more advanced topics, such as navigating data in Explore, tailoring dashboards to your needs, and adding dynamic elements for interactivity. Along the way, you'll gain invaluable troubleshooting skills to tackle common issues and optimize your Looker usage, ensuring a smooth and seamless experience. Furthermore, the book extends your understanding beyond the basics, equipping you with the knowledge you need to develop Looker applications and seamlessly integrate them with other tools and applications. You'll also explore advanced techniques for harnessing Looker's full potential, empowering you to establish data-driven decision-making and innovation within your organization.By the end of this BI book, you'll have gained a solid understanding of how to use Looker to find important information, make tasks easier, and derive important insights.What you will learnUnderstand Looker's key components, including LookML, data models, and dashboards.Explore Looker's functionality, including custom fields, calculations, and visualizations.Work with Looker dashboards using dynamic elements like links and actions.Use different types of filters for dimensions to create dashboardsDevelop Looker applications using essential tools and frameworksExplore additional applications for the Looker organizationIntegrate Looker with other tools using APIs, connectors, and data pipelinesWho this book is forIf you're a business analyst, data analyst, or BI developer who wants to get well-versed with the features of Looker, this book is for you. Basic knowledge of business intelligence is required to get started.Table of ContentsGetting started in Looker environmentConfiguring views and models in LookML projectWorking with data in ExploresCustomizing and serving the dashboardsMaking dashboards interactive through dynamic elementsTroubleshooting LookerPreparing to develop Looker applicationsOrganizing Looker environment Administering and Monitoring LookerIntegrating Looker with other Applications
Ultimate Microservices with Go
Develop production-ready, high-performance, and scalable microservices with GoBook DescriptionEmbark on a comprehensive journey through microservices architecture with a focus on harnessing the power of Go in modern cloud-based solutions.This book begins with a succinct introduction to microservices and their synergy with cloud strategies, emphasizing Go's aptitude for developing scalable and efficient services. You'll then delve into the fundamentals of Go, covering essential practices and core concepts, and establishing a strong language foundation.The exploration continues with a detailed examination of constructing a single service, emphasizing design, documentation, and structure. Through various design patterns, you'll learn to implement a server capable of serving as a RESTful API, an internal worker, and more. This hands-on approach equips you with the expertise to craft robust and sustainable services.Finally, the book guides you through deploying your service to production using Kubernetes. You'll explore scaling techniques, performance optimization, and observability, ensuring your service is ready for the demands of the real world.Table of Contents1. Introduction to Microservices2. Usability of Go3. Go Essentials4. Embarking on the Go Journey5. Unlocking Go's Concurrency Power6. Core Elements of Microservices7. Building RESTful API8. Introduction to Kubernetes9. Deploying to Production10. Next Steps in Production Index
Data Engineering with Google Cloud Platform - Second Edition
Become a successful data engineer by building and deploying your own data pipelines on Google Cloud, including making key architectural decisionsKey Features: - Get up to speed with data governance on Google Cloud- Learn how to use various Google Cloud products like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream- Boost your confidence by getting Google Cloud data engineering certification guidance from real exam experiences- Purchase of the print or Kindle book includes a free PDF eBookBook Description: The second edition of Data Engineering with Google Cloud builds upon the success of the first edition by offering enhanced clarity and depth to data professionals navigating the intricate landscape of data engineering.Beyond its foundational lessons, this new edition delves into the essential realm of data governance within Google Cloud, providing you with invaluable insights into managing and optimizing data resources effectively. Written by a Data Strategic Cloud Engineer at Google, this book helps you stay ahead of the curve by guiding you through the latest technological advancements in the Google Cloud ecosystem. You'll cover essential aspects, from exploring Cloud Composer 2 to the evolution of Airflow 2.5. Additionally, you'll explore how to work with cutting-edge tools like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream to perform data governance on datasets.By the end of this book, you'll be equipped to navigate the ever-evolving world of data engineering on Google Cloud, from foundational principles to cutting-edge practices.What You Will Learn: - Load data into BigQuery and materialize its output- Focus on data pipeline orchestration using Cloud Composer- Formulate Airflow jobs to orchestrate and automate a data warehouse- Establish a Hadoop data lake, generate ephemeral clusters, and execute jobs on the Dataproc cluster- Harness Pub/Sub for messaging and ingestion for event-driven systems- Apply Dataflow to conduct ETL on streaming data- Implement data governance services on Google CloudWho this book is for: Data analysts, IT practitioners, software engineers, or any data enthusiasts looking to have a successful data engineering career will find this book invaluable. Additionally, experienced data professionals who want to start using Google Cloud to build data platforms will get clear insights on how to navigate the path. Whether you're a beginner who wants to explore the fundamentals or a seasoned professional seeking to learn the latest data engineering concepts, this book is for you.Table of Contents- Fundamentals of Data engineering with GCP- Big Data Capabilities on GCP- Building a data warehouse in BigQuery- Build Orchestration for Batch Data Loading Using Cloud Composer- Building a Data Lake using Dataproc- Process Streaming Data with Datastream, Pub/Sub and Dataflow- Visualizing Data for Making Data-Driven Decisions with Looker Studio- Build machine learning solutions on GCP- User and Project Management on GCP- Data Governance in GCP- Cost Strategy in GCP- CI/CD on Google Cloud Platform for Data Engineers- Boost your confidence as a Data Engineer
Cultural Recycling in the Postdigital Age
This volume explores the development towards mass digitisation and datafication and its transforming influence on our way of organising our cultural knowledge and heritage. In this context, cultural recycling plays a crucial role, even if it is in itself not a new phenomenon. However, the quality and quantity of recycling processes have altered profoundly in the postdigital age. The contributions of this volume consider various manifestations of these recycling processes and practices by providing the reader with a wide range of different case studies. Their authors highlight characteristic features of postdigital recycling that differ from the qualities of recycling processes and practices in previous periods. What the case studies show are the different recyclings of canonical texts, folktales, and cultural productions in new postdigital environments, but also what happens to history and memory in today's times and even how self-declared pre-digital authors cannot escape postdigital strategies for cultural recycling.
HowExpert Guide to Artificial Intelligence
HowExpert Guide to Artificial Intelligence unfolds the complex world of artificial intelligence (AI), guiding readers through its transformative potential and the myriad ways it intersects with our lives. This insightful tome is crafted to demystify AI for a broad audience, from curious novices to seasoned experts, providing a deep dive into the essence, advancements, and ethical landscape of AI.- Embarks on a journey with a preface that illuminates AI's transformative path, setting the stage for a comprehensive exploration.- The introduction offers a clear delineation between AI myths and realities, establishing a foundation for the discussions that follow.- Traces the historical evolution of AI, highlighting key milestones and the visionaries who have propelled the field forward.- Breaks down the core technologies underpinning AI, such as machine learning and deep learning, explaining their significance and applications.- Explores the diverse applications of AI, from enhancing everyday life to revolutionizing industries like healthcare, finance, and manufacturing.- Discusses the profound ethical considerations and societal impacts of AI, including privacy concerns, job displacement, and the future of work.- Provides insights into the creation and management of AI solutions, detailing the tools, frameworks, and best practices for AI development.- Looks ahead to the future of AI research, contemplating the challenges and opportunities in striving for advancements and understanding human intelligence.- Envisions the future shaped by AI, pondering the societal transformations and ethical principles that will guide the development of AI technologies.- Concludes with reflections on the ongoing evolution of AI and its enduring significance for the future, encouraging readers to contemplate their role in an AI-driven world.- Appendices enhance the book's utility with a glossary of AI terms, a resource guide for further exploration, and interviews with experts, offering diverse perspectives on AI's present and future.HowExpert Guide to Artificial Intelligence is crafted as a dynamic resource for anyone seeking to understand or engage with artificial intelligence. With a balanced blend of technical insights, practical applications, and ethical reflections, this book not only educates but also inspires thoughtful consideration of AI's role in our collective future. It's an indispensable guide for those looking to grasp the complexities of AI and its potential to redefine the contours of human experience.HowExpert publishes how to guides on all topics from A to Z by everyday experts.
HowExpert Guide to Artificial Intelligence
HowExpert Guide to Artificial Intelligence unfolds the complex world of artificial intelligence (AI), guiding readers through its transformative potential and the myriad ways it intersects with our lives. This insightful tome is crafted to demystify AI for a broad audience, from curious novices to seasoned experts, providing a deep dive into the essence, advancements, and ethical landscape of AI.- Embarks on a journey with a preface that illuminates AI's transformative path, setting the stage for a comprehensive exploration.- The introduction offers a clear delineation between AI myths and realities, establishing a foundation for the discussions that follow.- Traces the historical evolution of AI, highlighting key milestones and the visionaries who have propelled the field forward.- Breaks down the core technologies underpinning AI, such as machine learning and deep learning, explaining their significance and applications.- Explores the diverse applications of AI, from enhancing everyday life to revolutionizing industries like healthcare, finance, and manufacturing.- Discusses the profound ethical considerations and societal impacts of AI, including privacy concerns, job displacement, and the future of work.- Provides insights into the creation and management of AI solutions, detailing the tools, frameworks, and best practices for AI development.- Looks ahead to the future of AI research, contemplating the challenges and opportunities in striving for advancements and understanding human intelligence.- Envisions the future shaped by AI, pondering the societal transformations and ethical principles that will guide the development of AI technologies.- Concludes with reflections on the ongoing evolution of AI and its enduring significance for the future, encouraging readers to contemplate their role in an AI-driven world.- Appendices enhance the book's utility with a glossary of AI terms, a resource guide for further exploration, and interviews with experts, offering diverse perspectives on AI's present and future.HowExpert Guide to Artificial Intelligence is crafted as a dynamic resource for anyone seeking to understand or engage with artificial intelligence. With a balanced blend of technical insights, practical applications, and ethical reflections, this book not only educates but also inspires thoughtful consideration of AI's role in our collective future. It's an indispensable guide for those looking to grasp the complexities of AI and its potential to redefine the contours of human experience.HowExpert publishes how to guides on all topics from A to Z by everyday experts.
The Relational Model for Database Management Version 2 - A Critical Analysis
The relational model of data was invented by one man, E. F. ("Ted") Codd, during his employment in the late 1960s and early 1970s at the IBM Research Laboratory in San Jose, California. Today, virtually all general purpose database systems are based on that model. But those systems are still a long way from perfect-and a major reason for this sorry state of affairs is that they none of them abide 100% by the prescriptions of the model. To address this problem, Codd subsequently wrote a book, The Relational Model for Database Management Version 2, in which he defined a hugely expanded version of his original model. His aim was to spell out, in vastly more detail than before, exactly what a relational database system is supposed to look like. Given his track record, anything Codd has to say regarding database matters clearly deserves very careful attention and analysis-and that's exactly what the present book provides regarding RM/V2 in particular: It describes, explains, and critiques Codd's new model in all of its myriad aspects. The author, Chris Date, was a close colleague and supporter of Codd's for many years and was instrumental in getting the word out regarding the original relational model.
Desktop Support Crash Course
����️ Desktop Support Crash Course Bundle! ����️Are you ready to take your IT skills to the next level? Introducing the Desktop Support Crash Course bundle, your ultimate guide to mastering technical problem-solving and network troubleshooting. ����With four comprehensive books packed with practical knowledge and expert insights, this bundle is your roadmap to becoming a desktop support superstar. �������� Book 1: Desktop Support Essentials Get started with the basics! Learn how to troubleshoot common hardware and software issues like a pro. From diagnosing system errors to resolving user problems, this beginner's guide has you covered.���� Book 2: Mastering Network Basics Unlock the secrets of networking fundamentals! Dive into IP addressing, subnetting, and network device configuration to ensure seamless connectivity in any environment.���� Book 3: Advanced Desktop Support Strategies Ready to level up? Delve deep into system diagnostics and advanced troubleshooting techniques. Discover how to analyze system logs, tune performance, and tackle complex hardware challenges with confidence.���� Book 4: Expert-Level Network Troubleshooting Become a networking ninja! Explore advanced protocols, packet analysis, and network security strategies to resolve even the trickiest issues. With insider tips and pro tricks, you'll be ready to conquer any network problem.���� What You'll Learn: - Troubleshooting principles and methodologies- Fundamental networking concepts- System diagnostics and performance tuning- Advanced network troubleshooting techniquesDon't miss out on this opportunity to become a desktop support expert! Grab your copy of the Desktop Support Crash Course bundle today and take the first step toward a rewarding career in IT support. ��������
Artificial Life and Evolutionary Computation
This book constitutes the refereed post proceedings of the 17th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2023, held in Venice, Italy, during September 6-8, 2023.The 30 full papers included in this book were carefully reviewed and selected from 55 submissions. They were organized in topical sections as follows: Algorithms for complex systems, Biologically inspired models, Complex chemical systems, Adaptation and swarms, Learning, Medicine and Social systems.
The Definitive Guide to Power Query (M)
Learn how to use the Power Query M formula language and its functions effectively for better data modeling and impactful business intelligence reports.Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesGet comprehensive coverage of fundamental and advanced Power Query conceptsGain hands-on experience with practical examples based on real-world problemsDelve into the intricacies of Power Query M language for enhanced data manipulationBook DescriptionData transformation is a critical step in building data models and business intelligence reports. Power Query is an invaluable tool for anyone who wants to master data transformation, and this book will equip you with the knowledge and skills to make the most of it.The Definitive Guide to Power Query (M) will help you build a solid foundation in the Power Query M language. As you progress through the chapters, you'll learn how to use that knowledge to implement advanced concepts and data transformations before going on a no-compromise 'deep dive' into the Power Query M language.You'll also get to grips with optimizing performance, handling errors, and implementing efficient data processing techniques. As this is a hands-on guide, the practical examples in the chapters will help you gain the skills to apply Power Query to real-world problems and improve your data analysis capabilities.By the end of this book, you will be able to leverage all of Power Query's remarkable capabilities for data transformationWhat you will learnGain a strong understanding of Power query fundamentalsMaster various functions within Power Query to perform complex data operationsAcquire knowledge about values, types, and control structures in Power QueryDevelop proficiency in error handling techniquesLearn performance optimization strategies for Power QueryApply what you've learned to real-world scenarios, including common troublesome patternsWho this book is forThis book is for business analysts, business intelligence professionals, and power business users working with data who want to add Power Query mastery to their resume. This book will be beneficial for anyone who wants to automate their process of data cleaning and save huge amount of time. Having some basic experience in Power Query is recommended.Table of ContentsIntroducing MWorking with Power Query/MAccessing and Combining DataUnderstanding Values and ExpressionsUnderstanding Data TypesStructured ValuesConceptualizing MWorking with Nested StructuresParameters and Custom FunctionsDealing with Dates, Times, and DurationsComparers, Replacers, Combiners, and SplittersHandling Errors and DebuggingIteration and RecursionTroublesome Data PatternsOptimizing PerformanceEnabling Extensions
The Definitive Guide to Data Integration
Learn the essentials of data integration with this comprehensive guide, covering everything from sources to solutions, and discover the key to making the most of your data stackKey FeaturesLearn how to leverage modern data stack tools and technologies for effective data integrationDesign and implement data integration solutions with practical advice and best practicesFocus on modern technologies such as cloud-based architectures, real-time data processing, and open-source tools and technologiesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe Definitive Guide to Data Integration is an indispensable resource for navigating the complexities of modern data integration. Focusing on the latest tools, techniques, and best practices, this guide helps you master data integration and unleash the full potential of your data. This comprehensive guide begins by examining the challenges and key concepts of data integration, such as managing huge volumes of data and dealing with the different data types. You'll gain a deep understanding of the modern data stack and its architecture, as well as the pivotal role of open-source technologies in shaping the data landscape. Delving into the layers of the modern data stack, you'll cover data sources, types, storage, integration techniques, transformation, and processing. The book also offers insights into data exposition and APIs, ingestion and storage strategies, data preparation and analysis, workflow management, monitoring, data quality, and governance. Packed with practical use cases, real-world examples, and a glimpse into the future of data integration, The Definitive Guide to Data Integration is an essential resource for data eclectics. By the end of this book, you'll have the gained the knowledge and skills needed to optimize your data usage and excel in the ever-evolving world of data.What you will learnDiscover the evolving architecture and technologies shaping data integrationProcess large data volumes efficiently with data warehousingTackle the complexities of integrating large datasets from diverse sourcesHarness the power of data warehousing for efficient data storage and processingDesign and optimize effective data integration solutionsExplore data governance principles and compliance requirementsWho this book is forThis book is perfect for data engineers, data architects, data analysts, and IT professionals looking to gain a comprehensive understanding of data integration in the modern era. Whether you're a beginner or an experienced professional enhancing your knowledge of the modern data stack, this definitive guide will help you navigate the data integration landscape.Table of ContentsIntroduction to our data integration journeyIntroducing Data IntegrationArchitecture and History of Data IntegrationData Sources and TypesColumnar Data Formats and comparisons Data Storage Technologies and ArchitecturesData Ingestion and Storage Strategies Data Integration TechniquesData Transformation and ProcessingTransformation patterns, cleansing and normalizationData Exposition and APIsData Preparation and AnalysisWorkflow Management, Monitoring, and Data QualityLineage, Governance, and ComplianceVarious Architecture Use CasesProspects and Challenges
Azure Data Factory Cookbook - Second Edition
Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data FactoryKey Features: Learn how to load and transform data from various sources, both on-premises and on cloudUse Azure Data Factory's visual environment to build and manage hybrid ETL pipelinesDiscover how to prepare, transform, process, and enrich data to generate key insightsBook Description: This new edition of the Azure Data Factory Cookbook, fully updated to reflect ADS V2, will help you get up and running by showing you how to create and execute your first job in ADF.You'll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discovering the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage.With practical recipes, you'll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premises infrastructure with cloud-native tools to get relevant business insights. As you advance, you'll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out how to use the Azure portal to monitor pipelines. You'll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF.Two new chapters covering Azure Data Explorer and key best practices have been added, along with new recipes throughout.By the end of this book, you'll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects.What You Will Learn: Create an orchestration and transformation job in ADFDevelop, execute, and monitor data flows using Azure SynapseCreate big data pipelines using Databricks and Delta tablesWork with big data in Azure Data Lake using Spark PoolMigrate on-premises SSIS jobs to ADFIntegrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure FunctionsRun big data compute jobs within HDInsight and Azure DatabricksCopy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectorsWho this book is for: This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone else who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You'll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is a prerequisite.
Artificial Intelligence - The Python Chatbot in Australia
At the age of 17 years old, Yeeshtdevisingh Hosanee had a profound interest in Computer Programming. She obtained a Computer Science BSc(Hons), before going on to gain her Master's in Software Engineering and a Master's in Business Administration specialisation in banking. Now, with over 15 years of professional experience, she continues her full-time job and maintains her passion for programming. This compels her to impart her knowledge and skills by being a part-time lecturer, contributing to academic research and non-profit organisations, pertaining to programming challenges, which in turn has led to her being recognised internationally by Agence Universitaire de la Francophonies (AUF-canada) and Junior Chamber International (JCI), as one of the top 30 finalists of the JCI TOYP world 2022. Since 2022, she had published four different books on "Learn Python Programming" and "Learn Java Programming", "Artificial Intelligence: The 10 Examples In Python" and "Machine Learning: The 10 Classifiers In Python". Programming has always been difficult for any age group. Yeeshtdevisingh's philosophy has always been to teach complicated concepts with analogies. In this book, she uses Australian cities as analogies, to support her examples.
Data Science
Welcome to the fascinating world of data science, where the art of extracting valuable insights from data meets the precision of advanced analytics. In this book, we embark on a comprehensive journey through the intricacies of data science, providing a hands-on guide for both beginners and seasoned professionals. Our primary focus is on practical applications and real-world problem-solving, employing the powerful R programming language to execute excursions into the realm of data analysis, visualization, and machine learning. As the demand for data-driven decision-making continues to rise across industries, mastering the tools and techniques of data science has become essential. This book is designed to equip you with the skills necessary to navigate the data landscape effectively, utilizing R as a versatile and robust tool. Whether you are exploring data for patterns, building predictive models, or creating compelling visualizations, the excursions in this book are crafted to empower you with the knowledge and confidence needed to extract meaningful insights and drive informed decisions. Let the journey begin, and may your exploration of data science through R programming be both enlightening and rewarding.
Applying Artificial Intelligence
Although AI technology has become mature, affordable, and robust and promises immense business value, implementations often fail with broken budgets and dispirited staff in its wake.According to the author, the problem is always the same. The project methods we use for designing and implementing traditional IT do not work for AI. Thus, the high failure rate is not caused by the technology but by how we run the projects.In the book, the author explains how AI differs from traditional IT. He then provides an easy-to-follow step-by-step method to identify the main challenges, write the business case, plan the project, design the solution, get the users on board, and deliver tangible business value.Finally, a book that shows you how to apply artificial intelligence to everyday problems. The author has deep practical experience with AI and generously shares it with his readers. ★★★★★ Martin Boberg, CEO, Diction--Dan's book brings us a practical approach to reap the potential of AI. Although the technology is sophisticated, he explains it simply and shows how it can be applied to solve critical business problems. I can recommend the book to anyone involved with business optimisation.★★★★★ Nicolai Hofs繪, Director of P2P, Vertical, Mobile & Fintech, Visma e-conomics--Over the years, I have read many books on AI, studied articles, taken academic courses, and spoken to countless fellow AI practitioners. This book sums it up neatly and gives valuable answers on practically implementing AI into your organisation.★★★★★ Christian M. Prip, Head of Data Consulting, Netcompany
Learn Microsoft Fabric
Harness the power of Microsoft Fabric to develop data analytics solutions for various use cases guided by step-by-step instructionsKey FeaturesExplore Microsoft Fabric and its features through real-world examplesBuild data analytics solutions for lakehouses, data warehouses, real-time analytics, and data scienceMonitor, manage, and administer your Fabric platform and analytics system to ensure flexibility, performance, security, and controlPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDiscover the capabilities of Microsoft Fabric, the premier unified solution designed for the AI era, seamlessly combining data integration, OneLake, transformation, visualization, universal security, and a unified business model. This book provides an overview of Microsoft Fabric, its components, and the wider analytics landscape.In this book, you'll explore workloads such as Data Factory, Synapse Data Engineering, data science, data warehouse, real-time analytics, and Power BI. You'll learn how to build end-to-end lakehouse and data warehouse solutions using the medallion architecture, unlock the real-time analytics, and implement machine learning and AI models. As you progress, you'll build expertise in monitoring workloads and administering Fabric across tenants, capacities, and workspaces. The book also guides you step by step through enhancing security and governance practices in Microsoft Fabric and implementing CI/CD workflows with Azure DevOps or GitHub. Finally, you'll discover the power of Copilot, an AI-driven assistant that accelerates your analytics journey.By the end of this book, you'll have unlocked the full potential of AI-driven data analytics, gaining a comprehensive understanding of the analytics landscape and mastery over the essential concepts and principles of Microsoft Fabric.What you will learnGet acquainted with the different services available in Microsoft FabricBuild end-to-end data analytics solution to scale and manage high performanceIntegrate data from different types of data sourcesApply transformation with Spark, Notebook, and T-SQLUnderstand and implement real-time stream processing and data science capabilitiesPerform end-to-end processes for building data analytics solutions in the AI eraDrive insights by leveraging Power BI for reporting and visualizationImprove productivity with AI assistance and Copilot integrationWho this book is forThis book is for data professionals, including data analysts, data engineers, data scientists, data warehouse developers, ETL developers, business analysts, AI/ML professionals, software developers, and Chief Data Officers who want to build a future-ready data analytics solution for long-term success in the AI era.For PySpark and SQL students entering the data analytics field, this book offers a broad foundation for developing the skills to build end-to-end analytics systems for various use cases. Basic knowledge of SQL and Spark is assumed.Table of ContentsOverview of Microsoft Fabric and Understanding Its Different ConceptsUnderstanding Different Workloads and Getting Started with Microsoft Fabric Building an End-to-End Analytics System - LakehouseBuilding an End-to-End Analytics System - Data WarehouseBuilding an End-to-End Analytics System - Real-Time AnalyticsBuilding an End-to-End Analytics System - Data ScienceMonitoring Overview and Monitoring Different WorkloadsAdministering FabricSecurity and Governance Overview(N.B. Please use the Look Inside option to see further chapters)
Federated Learning
Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data. Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors.
Learn T-SQL Querying - Second Edition
Troubleshoot query performance issues, identify anti-patterns in your code, and write efficient T-SQL queries with this guide for T-SQL developersKey Features: - A definitive guide to mastering the techniques of writing efficient T-SQL code- Learn query optimization fundamentals, query analysis, and how query structure impacts performance- Discover insightful solutions to detect, analyze, and tune query performance issues- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Data professionals seeking to excel in Transact-SQL for Microsoft SQL Server and Azure SQL Database often lack comprehensive resources. Learn T-SQL Querying second edition focuses on indexing queries and crafting elegant T-SQL code enabling data professionals gain mastery in modern SQL Server versions (2022) and Azure SQL Database. The book covers new topics like logical statement processing flow, data access using indexes, and best practices for tuning T-SQL queries.Starting with query processing fundamentals, the book lays a foundation for writing performant T-SQL queries. You'll explore the mechanics of the Query Optimizer and Query Execution Plans, learning to analyze execution plans for insights into current performance and scalability. Using dynamic management views (DMVs) and dynamic management functions (DMFs), you'll build diagnostic queries. The book covers indexing and delves into SQL Server's built-in tools to expedite resolution of T-SQL query performance and scalability issues. Hands-on examples will guide you to avoid UDF pitfalls and understand features like predicate SARGability, Query Store, and Query Tuning Assistant.By the end of this book, you'll have developed the ability to identify query performance bottlenecks, recognize anti-patterns, and avoid pitfallsWhat You Will Learn: - Identify opportunities to write well-formed T-SQL statements- Familiarize yourself with the Cardinality Estimator for query optimization- Create efficient indexes for your existing workloads- Implement best practices for T-SQL querying- Explore Query Execution Dynamic Management Views- Utilize the latest performance optimization features in SQL Server 2017, 2019, and 2022- Safeguard query performance during upgrades to newer versions of SQL ServerWho this book is for: This book is for database administrators, database developers, data analysts, data scientists and T-SQL practitioners who want to master the art of writing efficient T-SQL code and troubleshooting query performance issues through practical examples. A basic understanding of T-SQL syntax, writing queries in SQL Server, and using the SQL Server Management Studio tool will be helpful to get started.Table of Contents- Understanding Query Processing- Mechanics of the Query Optimizer- Exploring Query Execution Plans- Indexing for T-SQL Performance- Writing Elegant T-SQL Queries- Discovering T-SQL Anti-patterns in Depth - Building diagnostic queries using DMVs and DMFs- Building XEvent profiler traces- Comparative analysis of Query Plans- Tracking performance history with Query Store- Troubleshooting Live Queries- Managing optimizer changes with the Query Tuning Assistant
Artificial Intelligence for Social Good (Hardcover Edition)
"Artificial Intelligence for Social Good" is a comprehensive overview of AI's potential to address global issues of poverty, health, education, environment, and human rights, offering insights into ethical considerations and best practices, making it a valuable resource for anyone interested in leveraging AI for positive social impact.
Ethical AI
Unlock the mysteries of AI and use this technology to your advantage - step into the future of limitless possibilities! Are you intrigued by Artificial Intelligence (AI) but feel overwhelmed by its complexity? Do you wonder how AI could transform your job or industry yet struggle to keep pace with its rapid advancements? Or are you afraid of AI taking over jobs, controlling minds, or destroying humanity - just like in the movies? The world of AI can be both intriguing and overwhelming. A recent poll of over 4,000 adults in the United States revealed some startling sentiments: 61% believe that AI could threaten the future of civilization, and over two-thirds express concern about AI's potential negative consequences. With the rapid pace of advancements and seemingly alien terminology, it's easy to see why the landscape of AI might seem unsettling, daunting even. But what if we could turn that fear into fascination? What if the complex world of AI was broken down into manageable, relatable pieces, transforming confusion into clarity? Picture yourself at the forefront of the technological revolution, leveraging AI's potential to drive innovation, solve real-world problems, and shape the future. This book opens the door to not only understanding AI but also harnessing its capabilities and applying them in your work, irrespective of your industry. Inside, you will discover: ● The key to deciphering the world of AI, making complex concepts accessible and fascinating - get to know the amazing capabilities of thinking machines!● The inner workings of Machine Learning and Deep Learning - be fascinated by how they serve as AI's foundational pillars!● Practical, transformative applications of AI across multiple industries including yours - you will be surprised at how AI is basically everywhere!● Step-by-step guidelines to design and implement effective AI solutions in your workplace - adapt the technology that can lead your organization to success!● How to identify and mitigate AI bias, navigating the ethical dilemmas AI brings - discussing these complicated topics is important for better understanding● How AI intersects with cybersecurity, revolutionizing modern defense strategies - gone are the days where cybercriminals have the upper hand● The future trends of AI, empowering you to stay updated in this rapidly evolving field - AI is definitely here to stay! And much more! You don't need to become an information technology professional or computer scientist to be able to understand this technology. The book breaks complex jargon down for you in an engaging and simple manner - so you can take advantage of AI and its practical applications, no matter what role or industry you're in.
