"Extract, transform, load" (ETL) is at the center of every application of data, from business intelligence to AI. Constant shifts in the data landscape—including the implementations of lakehouse architectures and the importance of high-scale real-time data—mean that today's data practitioners must approach ETL a bit differently. This updated technical guide offers data engineers, engineering managers, and architects an overview of the modern ETL process, along with the challenges you're likely to face and the strategic patterns that will help you overcome them. You'll come away equipped to make informed decisions when implementing ETL and confident about choosing the technology stack that will help you succeed. Discover what ETL looks like in the new world of data lakehouses Learn how to deal with real-time data Explore low-code ETL tools Understand how to best achieve scale, performance, and observability

The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX! Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python. In Deep Learning with Python, Third Edition you’ll discover: Deep learning from first principles The latest features of Keras 3 A primer on JAX, PyTorch, and TensorFlow Image classification and image segmentation Time series forecasting Large Language models Text classification and machine translation Text and image generation—build your own GPT and diffusion models! Scaling and tuning models With over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images. About the Technology In less than a decade, deep learning has changed the world—twice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider. About the Book Deep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer. What's Inside Hands-on, code-first learning Comprehensive, from basics to generative AI Intuitive and easy math explanations Examples in Keras, PyTorch, JAX, and TensorFlow About the Reader For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required. About the Authors François Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras. Quotes Perfect for anyone interested in learning by doing from one of the industry greats. - Anthony Goldbloom, Founder of Kaggle A sharp, deeply practical guide that teaches you how to think from first principles to build models that actually work. - Santiago Valdarrama, Founder of ml.school The most up-to-date and complete guide to deep learning you’ll find today! - Aran Komatsuzaki, EleutherAI Masterfully conveys the true essence of neural networks. A rare case in recent years of outstanding technical writing. - Salvatore Sanfilippo, Creator of Redis

Put your prompt-writing skills to the test in a friendly, fast-paced contest. You’ll work with a ring-fenced large language model (LLM) and a shared dataset, racing to surface the right answers as the questions get tougher. Think of it as a pub quiz for data folk – but the questions are answered with code-like prompts. Quick briefing – we’ll show you the dataset, the rules and a few prompt-engineering tips. Answer the questions – each round ups the difficulty, challenging you to refine, chain or re-use prompts in inventive ways. Leaderboard & prizes – points for accuracy and ingenuity. Top spot takes home bragging rights and a tidy prize.

Comprehensive guide offering actionable strategies for enhancing human-centered AI, efficiency, and productivity in industrial and systems engineering through the power of AI. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is the first book in the Advances in Industrial and Systems Engineering series, offering insights into AI techniques, challenges, and applications across various industrial and systems engineering (ISE) domains. Not only does the book chart current AI trends and tools for effective integration, but it also raises pivotal ethical concerns and explores the latest methodologies, tools, and real-world examples relevant to today’s dynamic ISE landscape. Readers will gain a practical toolkit for effective integration and utilization of AI in system design and operation. The book also presents the current state of AI across big data analytics, machine learning, artificial intelligence tools, cloud-based AI applications, neural-based technologies, modeling and simulation in the metaverse, intelligent systems engineering, and more, and discusses future trends. Written by renowned international contributors for an international audience, Advances in Artificial Intelligence Applications in Industrial and Systems Engineering includes information on: Reinforcement learning, computer vision and perception, and safety considerations for autonomous systems (AS) (NLP) topics including language understanding and generation, sentiment analysis and text classification, and machine translation AI in healthcare, covering medical imaging and diagnostics, drug discovery and personalized medicine, and patient monitoring and predictive analysis Cybersecurity, covering threat detection and intrusion prevention, fraud detection and risk management, and network security Social good applications including poverty alleviation and education, environmental sustainability, and disaster response and humanitarian aid. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is a timely, essential reference for engineering, computer science, and business professionals worldwide.

Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern emerge: AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations. While these agents enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks, and deploying them remains a challenge for many organizations, especially as technology and research rapidly develops. This book is your indispensable guide through this intricate and fast-moving landscape. Author Michael Albada provides a practical and research-based approach to designing and implementing single- and multiagent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently. Understand the distinct features of foundation model-enabled AI agents Discover the core components and design principles of AI agents Explore design trade-offs and implement effective multiagent systems Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field

There are already excellent books on software programming for data processing and data transformation for instance: Wes McKinney’s. This book, reflecting on my own industrial and teaching experience, tries to overcome the big learning curve newcomers to the field have to travel before they are ready to tackle real data science and AI challenges. In this regard this book is different to other books in that:

It assumes zero software programming knowledge. This instructional design is intentional given the book’s aim to open the practice of data science to anyone interested in data exploration and analysis irrespective of their previous background.

It follows an incremental approach to facilitate the assimilation of, sometimes, arcane software techniques to manipulate data.

It is practice oriented to ensure readers can apply what they learn in their daily practices.

Illustrates how to use generative AI to help you become a more productive data scientist and AI engineer.

By reading and working on the labs included in this book you will develop software programming skills required to successfully contribute to the data understanding and data preparation stages involved in any data related project. You will become proficient at manipulating and transforming datasets in industrial contexts and produce clean, reliable datasets that can drive accurate analysis and informed decision-making. Moreover you will be prepared to develop and deploy dashboards and visualizations supporting the insights and conclusions in the deployment stage.

Data modelling and evaluation are not covered in this book. We are working on a second installment of the book series illustrating the application of statistical and machine learning techniques to derive data insights.

Revolutionize your understanding of modern data management with Apache Polaris (incubating), the open source catalog designed for data lakehouse industry standard Apache Iceberg. This comprehensive guide takes you on a journey through the intricacies of Apache Iceberg data lakehouses, highlighting the pivotal role of Iceberg catalogs. Authors Alex Merced, Andrew Madson, and Tomer Shiran explore Apache Polaris's architecture and features in detail, equipping you with the knowledge needed to leverage its full potential. Data engineers, data architects, data scientists, and data analysts will learn how to seamlessly integrate Apache Polaris with popular data tools like Apache Spark, Snowflake, and Dremio to enhance data management capabilities, optimize workflows, and secure datasets. Get a comprehensive introduction to Iceberg data lakehouses Understand how catalogs facilitate efficient data management and querying in Iceberg Explore Apache Polaris's unique architecture and its powerful features Deploy Apache Polaris locally, and deploy managed Apache Polaris from Snowflake and Dremio Perform basic table operations on Apache Spark, Snowflake, and Dremio

Initiation workshop in code or data as part of the open day event.

The book provides essential insights and practical tools needed to effectively navigate the evolving landscape of scholarly research, helping enhance the understanding of publication trends, citation impacts, and collaboration networks across multiple fields. Bibliometric Analyses in Data-Driven Decision-Making offers a comprehensive guide to researchers, academics, and practitioners interested in utilizing bibliometric analysis to understand and navigate the dynamic landscape of the increasingly vital field of data-driven decision-making and its applications across many areas. It provides insights into growth, impact, and trends within the field, using bibliometric tools and methodologies. This volume adopts a pragmatic approach, balancing theoretical concepts with practical applications of data-driven decision-making models through the perspectives of bibliometric analyses using real-world examples, case studies, and step-by-step guides. The reader will find the book: Gives practical guidance on conducting bibliometric analyses across a range of applications for data-driven decision-making; Illustrates the application of bibliometric tools in the field with real-world case studies; Provides in-depth coverage of various bibliometric indicators and metrics; Explores emerging trends and challenges in bibliometric analysis; Provides a comprehensive overview of software and tools available for bibliometric research. Audience Librarians and Information professionals involved in research management, knowledge discovery, and the evaluation of scholarly communication, as well as professionals in industries reliant on cutting-edge research and development, technology assessment, and innovation. Also, a range of researchers and scholars seeking how to apply bibliometric analysis to assess the impact of their work, and advanced insights into bibliometric metrics, collaboration networks, and research trends.

Practical strategies to help you design, optimize, and operate MongoDB deployments for performance, resilience, and growth Key Features Identify and fix performance bottlenecks with practical diagnostic and optimization strategies Optimize schema design, indexing, storage, and system resources for real-world workloads Scale confidently with in-depth coverage of replication, sharding, and cluster management techniques Purchase of the print or Kindle book includes a free PDF eBook Book Description With data as the new competitive edge, performance has become the need of the hour. As applications handle exponentially growing data and user demand for speed and reliability rises, three industry experts distill their decades of experience to offer you guidance on designing, building, and operating databases that deliver fast, scalable, and resilient experiences. MongoDB’s document model and distributed architecture provide powerful tools for modern applications, but unlocking their full potential requires a deep understanding of architecture, operational patterns, and tuning best practices. This MongoDB book takes a hands-on approach to diagnosing common performance issues and applying proven optimization strategies from schema design and indexing to storage engine tuning and resource management. Whether you’re optimizing a single replica set or scaling a sharded cluster, this book provides the tools to maximize deployment performance. Its modular chapters let you explore query optimization, connection management, and monitoring or follow a complete learning path to build a rock-solid performance foundation. With real-world case studies, code examples, and proven best practices, you’ll be ready to troubleshoot bottlenecks, scale efficiently, and keep MongoDB running at peak performance in even the most demanding production environments. What you will learn Diagnose and resolve common performance bottlenecks in deployments Design schemas and indexes that maximize throughput and efficiency Tune the WiredTiger storage engine and manage system resources for peak performance Leverage sharding and replication to scale and ensure uptime Monitor, debug, and maintain deployments proactively to prevent issues Improve application responsiveness through client driver configuration Who this book is for This book is for developers, database administrators, system architects, and DevOps engineers focused on performance optimization of MongoDB. Whether you’re building high-throughput applications, managing deployments in production, or scaling distributed systems, you’ll gain actionable insights. Basic knowledge of MongoDB is assumed, with chapters designed progressively to support learners at all levels.

Get started fast with MongoDB architecture, core operations, and AI-powered tools for building intelligent applications Free with your book: DRM-free PDF version + access to Packt's next-gen Reader Key Features Quickly grasp the MongoDB architecture and distributed design principles Learn practical data modeling, CRUD operations, and aggregation techniques Explore AI-enabled tools for building intelligent applications with MongoDB Purchase of the print or Kindle book includes a free PDF eBook Book Description Modern applications demand flexibility, speed, and intelligence, and MongoDB delivers all three. This mini guide wastes no time, offering a concise, practical introduction to handling data flexibly and efficiently with MongoDB. MongoDB Essentials helps developers, architects, database administrators, and decision makers get started quickly and confidently. The book introduces MongoDB’s core principles, from the document data model to its distributed architecture, including replica sets and sharding. It then helps you build hands-on skills such as installing MongoDB, designing effective data schemas, performing CRUD operations, and working with the aggregation pipeline. You’ll discover performance tips along the way and learn how AI-enhanced tools like Atlas Search and Atlas Vector Search power intelligent application development. With clear explanations and a practical approach, this book gives you the foundation and skills you need to start working with MongoDB right away. Email sign-up and proof of purchase required What you will learn Understand MongoDB's document model and architecture Set up local MongoDB deployments quickly Design schemas tailored to application access patterns Perform CRUD and aggregation operations efficiently Use tools to optimize query performance and scalability Explore AI-powered features such as Atlas Search and Atlas Vector Search Who this book is for This book is for anyone looking to explore MongoDB, including students, developers, system architects, managers, database administrators, and decision makers who want to familiarize themselves with what a modern database can offer. Whether you're building your first application or exploring what MongoDB can do for you, this book is the idea starting point for your MongoDB journey.

The official guide to MongoDB architecture, tools, and cloud features, written by leading MongoDB subject matter experts to help you build secure, scalable, high-performance applications Key Features Design resilient, secure solutions with high performance and scalability Streamline development with modern tooling, indexing, and AI-powered workflows Deploy and optimize in the cloud using advanced MongoDB Atlas features Purchase of the print or Kindle book includes a free PDF eBook Book Description Delivering secure, scalable, and high-performance applications is never easy, especially when systems must handle growth, protect sensitive data, and perform reliably under pressure. The Official MongoDB Guide addresses these challenges with guidance from MongoDB’s top subject matter experts, so you learn proven best practices directly from those who know the technology inside out. This book takes you from core concepts and architecture through to advanced techniques for data modeling, indexing, and query optimization, supported by real-world patterns that improve performance and resilience. It offers practical coverage of developer tooling, IDE integrations, and AI-assisted workflows that will help you work faster and more effectively. Security-focused chapters walk you through authentication, authorization, encryption, and compliance, while chapters dedicated to MongoDB Atlas showcase its robust security features and demonstrate how to deploy, scale, and leverage platform-native capabilities such as Atlas Search and Atlas Vector Search. By the end of this book, you’ll be able to design, build, and manage MongoDB applications with the confidence that comes from learning directly from the experts shaping the technology. What you will learn Build secure, scalable, and high-performance applications Design efficient data models and indexes for real workloads Write powerful queries to sort, filter, and project data Protect applications with authentication and encryption Accelerate coding with AI-powered and IDE-based tools Launch, scale, and manage MongoDB Atlas with confidence Unlock advanced features like Atlas Search and Atlas Vector Search Apply proven techniques from MongoDB's own engineering leaders Who this book is for This book is for developers, database professionals, architects, and platform teams who want to get the most out of MongoDB. Whether you’re building web apps, APIs, mobile services, or backend systems, the concepts covered here will help you structure data, improve performance, and deliver value to your users. No prior experience with MongoDB is required, but familiarity with databases and programming will be helpful.

Data Modeling with Snowflake provides a clear and practical guide to mastering data modeling tailored to the Snowflake Data Cloud. By integrating foundational principles of database modeling with Snowflake's unique features and functionality, this book empowers you to create scalable, cost-effective, and high-performing data solutions. What this Book will help me do Apply universal data modeling concepts within the Snowflake platform effectively. Leverage Snowflake's features such as Time Travel and Zero-Copy Cloning for optimized data solutions. Understand and utilize advanced techniques like Data Vault and Data Mesh for scalable data architecture. Master handling semi-structured data in Snowflake using practical recipes and examples. Achieve cost efficiency and resource optimization by aligning modeling principles with Snowflake's architecture. Author(s) Serge Gershkovich is an accomplished data engineer and seasoned professional in data architecture and modeling. With a passion for simplifying complex concepts, Serge's work leverages his years of hands-on experience to guide readers in mastering both foundational and advanced data management practices. His clear and practical approach ensures accessibility for all levels. Who is it for? This book is ideal for data developers and engineers seeking practical modeling guidance within Snowflake. It's suitable for data analysts looking to broaden their database design expertise, and for database beginners aiming to get a head start in structuring data. Professionals new to Snowflake will also find its clear explanations of key features aligned with modeling techniques invaluable.

Learn how to harness the power of OpenSearch effectively with 'The Definitive Guide to OpenSearch'. This book explores installation, configuration, query building, and visualization, guiding readers through practical use cases and real-world implementations. Whether you're building search experiences or analyzing data patterns, this guide equips you thoroughly. What this Book will help me do Understand core OpenSearch principles, architecture, and the mechanics of its search and analytics capabilities. Learn how to perform data ingestion, execute advanced queries, and produce insightful visualizations on OpenSearch Dashboards. Implement scaling strategies and optimum configurations for high-performance OpenSearch clusters. Explore real-world case studies that demonstrate OpenSearch applications in diverse industries. Gain hands-on experience through practical exercises and tutorials for mastering OpenSearch functionality. Author(s) Jon Handler, Soujanya Konka, and Prashant Agrawal, celebrated experts in search technologies and big data analysis, bring their years of experience at AWS and other domains to this book. Their collective expertise ensures that readers receive both core theoretical knowledge and practical applications to implement directly. Who is it for? This book is aimed at developers, data professionals, engineers, and systems operators who work with search systems or analytics platforms. It is especially suitable for individuals in roles handling large-scale data, who want to improve their skills or deploy OpenSearch in production environments. Early learners and seasoned experts alike will find valuable insights.

Embark on a journey to master business process modeling and analysis with this comprehensive guide. Through practical examples and structured frameworks, this book helps you learn to define, map, and optimize your business processes for digital transformation. By the end, you'll be equipped to drive seamless integration of automation and align processes with strategic goals. What this Book will help me do Become proficient in using BPMN for modeling complex business processes effectively. Develop skills to identify inefficiencies and optimize business processes for measurable improvements. Understand how to integrate automation into processes to enhance operational efficiency. Learn to evaluate business process performance and align changes with business goals. Apply frameworks and best practices for successful digital transformation. Author(s) The authors, Jim Sinur, Zbigniew Misiak, and BJ Biernatowski, bring decades of experience in business process modeling, automation, and consulting. They've guided organizations through challenging transformations and are experts in leveraging BPMN and related technologies. Their insights in this book stem from real-world challenges and successes, providing readers with practical and actionable guidance. Who is it for? This book is tailored for business analysts, process improvement practitioners, project managers, consultants, operations managers, and IT leaders. Whether you are starting with no prior experience in BPMN or looking to enhance your existing skillset, this book offers valuable insights for streamlining workflows and driving AI-powered innovation.

Discover how to build autonomous AI agents tailored for real-world tasks with 'AI Agents in Practice.' This book guides you through creating and deploying AI systems that go beyond chatbots to solve complex problems, using leading frameworks and practical design patterns. What this Book will help me do Understand and implement core components of AI agents, such as memory, tool integration, and context management. Develop production-ready AI agents for diverse applications using frameworks like LangChain. Design and implement multi-agent systems to enable advanced collaboration and problem-solving. Apply ethical and responsible AI techniques, including monitoring and human oversight, in agent development. Optimize performance and scalability of AI agents for production use cases. Author(s) Valentina Alto is an accomplished AI engineer with years of experience in AI systems design and implementation. Valentina specializes in developing practical solutions utilizing large language models and contemporary frameworks for real-world applications. Through her writing, she conveys complex ideas in an accessible manner, and her goal is to empower AI developers and enthusiasts with the skills to create meaningful solutions. Who is it for? This book is perfect for AI engineers, data scientists, and software developers ready to go beyond foundational knowledge of large language models to implement advanced AI agents. It caters to professionals looking to build scalable solutions and those interested in ethical considerations of AI usage. Readers with a background in machine learning and Python will benefit most from the technical insights provided.

Security teams rely on telemetry—the continuous stream of logs, events, metrics, and signals that reveal what’s happening across systems, endpoints, and cloud services. But that data doesn’t organize itself. It has to be collected, normalized, enriched, and secured before it becomes useful. That’s where data engineering comes in. In this hands-on guide, cybersecurity engineer James Bonifield teaches you how to design and build scalable, secure data pipelines using free, open source tools such as Filebeat, Logstash, Redis, Kafka, and Elasticsearch and more. You’ll learn how to collect telemetry from Windows including Sysmon and PowerShell events, Linux files and syslog, and streaming data from network and security appliances. You’ll then transform it into structured formats, secure it in transit, and automate your deployments using Ansible. You’ll also learn how to: Encrypt and secure data in transit using TLS and SSH Centrally manage code and configuration files using Git Transform messy logs into structured events Enrich data with threat intelligence using Redis and Memcached Stream and centralize data at scale with Kafka Automate with Ansible for repeatable deployments Whether you’re building a pipeline on a tight budget or deploying an enterprise-scale system, this book shows you how to centralize your security data, support real-time detection, and lay the groundwork for incident response and long-term forensics.

Unlock the future of technology and sustainable development by purchasing Metaverse for Sustainable Development: Trends and Applications, a comprehensive guide that delves into immersive application building, groundbreaking innovations, and the transformative potential of the metaverse across various industries. Metaverse for Sustainable Development: Trends and Applications explains the fine details of metaverse application building, demonstrating how integrated platforms in association with a suite of tools come in handy for enabling application construction. The metaverse is the next big thing influenced by virtual and augmented reality paradigms. This user experience will be more immersive and mesmerizing, empowering innovative, disruptive, and transformative technologies to create a spectacular platform for visualizing and realizing business-critical and people-centric metaverse systems. This book explores various metaverse models for healthcare information systems, including the latest technologies, such as the Brain-Computer Interface. Through real-world data and case studies, readers will gain a comprehensive understanding of the metaverse’s potential for the Internet of Things, blockchain, artificial intelligence, 5G, and 3D modelling for creating and sustaining immersive virtual worlds. Metaverse for Sustainable Development: Trends and Applications is a vital resource for understanding the end-to-end implementation of metaverse technologies.

Presents a structured approach to privacy management, an indispensable resource for safeguarding data in an ever-evolving digital landscape In today’s data-driven world, protecting personal information has become a critical priority for organizations of all sizes. Building Effective Privacy Programs: Cybersecurity from Principles to Practice equips professionals with the tools and knowledge to design, implement, and sustain robust privacy programs. Seamlessly integrating foundational principles, advanced privacy concepts, and actionable strategies, this practical guide serves as a detailed roadmap for navigating the complex landscape of data privacy. Bridging the gap between theoretical concepts and practical implementation, Building Effective Privacy Programs combines in-depth analysis with practical insights, offering step-by-step instructions on building privacy-by-design frameworks, conducting privacy impact assessments, and managing compliance with global regulations. In-depth chapters feature real-world case studies and examples that illustrate the application of privacy practices in a variety of scenarios, complemented by discussions of emerging trends such as artificial intelligence, blockchain, IoT, and more. Providing timely and comprehensive coverage of privacy principles, regulatory compliance, and actionable strategies, Building Effective Privacy Programs: Addresses all essential areas of cyberprivacy, from foundational principles to advanced topics Presents detailed analysis of major laws, such as GDPR, CCPA, and HIPAA, and their practical implications Offers strategies to integrate privacy principles into business processes and IT systems Covers industry-specific applications for healthcare, finance, and technology sectors Highlights successful privacy program implementations and lessons learned from enforcement actions Includes glossaries, comparison charts, sample policies, and additional resources for quick reference Written by seasoned professionals with deep expertise in privacy law, cybersecurity, and data protection, Building Effective Privacy Programs: Cybersecurity from Principles to Practice is a vital reference for privacy officers, legal advisors, IT professionals, and business executives responsible for data governance and regulatory compliance. It is also an excellent textbook for advanced courses in cybersecurity, information systems, business law, and business management.

Uncover the power of Graph Query Language (GQL) with 'Getting Started with the Graph Query Language'. This book is your comprehensive guide to mastering GQL, the cornerstone of managing and analyzing complex graph data. Dive into foundational concepts, explore advanced capabilities, and apply them using real-world examples. What this Book will help me do Understand and use GQL syntax effectively, including commands like MATCH, RETURN, INSERT, and DELETE. Master operations with graph patterns, variables, and functions to manipulate and query graph data. Apply advanced GQL techniques such as path matching modes, shortest paths, and transaction commands. Optimize graph database performance using indexing or caching strategies. Utilize GQL on a practical application, such as analyzing money transaction data for behavior and risk insights. Author(s) Ricky Sun, Jason Zhang, and Yuri Simione are seasoned experts in graph database technologies and standards. With years of professional experience and a collaborative spirit, they bring clarity and practice-oriented guidance to understanding GQL. Their passion for teaching and simplifying complex ideas shines through this well-crafted book. Who is it for? This book is ideal for graph database developers, database administrators, and data engineers looking to grasp GQL's fundamentals and advanced features. Beginners familiar with databases and programming fundamentals can follow along seamlessly. It also appeals to analysts and programmers seeking to enhance their graph data handling skills. Prior knowledge of graph theory concepts like nodes and edges is helpful but not mandatory, ensuring accessibility for learners of diverse levels.