Snowflake is reshaping data management by integrating AI, analytics, and enterprise workloads into a single cloud platform. Snowflake: The Definitive Guide is a comprehensive resource for data architects, engineers, and business professionals looking to harness Snowflake's evolving capabilities, including Cortex AI, Snowpark, and Polaris Catalog for Apache Iceberg. This updated edition provides real-world strategies and hands-on activities for optimizing performance, securing data, and building AI-driven applications. With hands-on SQL examples and best practices, this book helps readers process structured and unstructured data, implement scalable architectures, and integrate Snowflake's AI tools seamlessly. Whether you're setting up accounts, managing access controls, or leveraging generative AI, this guide equips you with the expertise to maximize Snowflake's potential. Implement AI-powered workloads with Snowflake Cortex Explore Snowsight and Streamlit for no-code development Ensure security with access control and data governance Optimize storage, queries, and computing costs Design scalable data architectures for analytics and machine learning

A shift is underway in how organizations approach data infrastructure for AI-driven transformation. As multimodal AI systems and applications become increasingly sophisticated and data hungry, data systems must evolve to meet these complex demands. Data Engineering for Multimodal AI is one of the first practical guides for data engineers, machine learning engineers, and MLOps specialists looking to rapidly master the skills needed to build robust, scalable data infrastructures for multimodal AI systems and applications. You'll follow the entire lifecycle of AI-driven data engineering, from conceptualizing data architectures to implementing data pipelines optimized for multimodal learning in both cloud native and on-premises environments. And each chapter includes step-by-step guides and best practices for implementing key concepts. Design and implement cloud native data architectures optimized for multimodal AI workloads Build efficient and scalable ETL processes for preparing diverse AI training data Implement real-time data processing pipelines for multimodal AI inference Develop and manage feature stores that support multiple data modalities Apply data governance and security practices specific to multimodal AI projects Optimize data storage and retrieval for various types of multimodal ML models Integrate data versioning and lineage tracking in multimodal AI workflows Implement data-quality frameworks to ensure reliable outcomes across data types Design data pipelines that support responsible AI practices in a multimodal context

Master end-to-end data engineering on Azure Databricks. From data ingestion and Delta Lake to CI/CD and real-time streaming, build secure, scalable, and performant data solutions with Spark, Unity Catalog, and ML tools. Key Features Build scalable data pipelines using Apache Spark and Delta Lake Automate workflows and manage data governance with Unity Catalog Learn real-time processing and structured streaming with practical use cases Implement CI/CD, DevOps, and security for production-ready data solutions Explore Databricks-native ML, AutoML, and Generative AI integration Book Description "Data Engineering with Azure Databricks" is your essential guide to building scalable, secure, and high-performing data pipelines using the powerful Databricks platform on Azure. Designed for data engineers, architects, and developers, this book demystifies the complexities of Spark-based workloads, Delta Lake, Unity Catalog, and real-time data processing. Beginning with the foundational role of Azure Databricks in modern data engineering, you’ll explore how to set up robust environments, manage data ingestion with Auto Loader, optimize Spark performance, and orchestrate complex workflows using tools like Azure Data Factory and Airflow. The book offers deep dives into structured streaming, Delta Live Tables, and Delta Lake’s ACID features for data reliability and schema evolution. You’ll also learn how to manage security, compliance, and access controls using Unity Catalog, and gain insights into managing CI/CD pipelines with Azure DevOps and Terraform. With a special focus on machine learning and generative AI, the final chapters guide you in automating model workflows, leveraging MLflow, and fine-tuning large language models on Databricks. Whether you're building a modern data lakehouse or operationalizing analytics at scale, this book provides the tools and insights you need. What you will learn Set up a full-featured Azure Databricks environment Implement batch and streaming ingestion using Auto Loader Optimize Spark jobs with partitioning and caching Build real-time pipelines with structured streaming and DLT Manage data governance using Unity Catalog Orchestrate production workflows with jobs and ADF Apply CI/CD best practices with Azure DevOps and Git Secure data with RBAC, encryption, and compliance standards Use MLflow and Feature Store for ML pipelines Build generative AI applications in Databricks Who this book is for This book is for data engineers, solution architects, cloud professionals, and software engineers seeking to build robust and scalable data pipelines using Azure Databricks. Whether you're migrating legacy systems, implementing a modern lakehouse architecture, or optimizing data workflows for performance, this guide will help you leverage the full power of Databricks on Azure. A basic understanding of Python, Spark, and cloud infrastructure is recommended.

A guide to pass the GCP Professional Data Engineer exam on your first attempt and upgrade your data engineering skills on GCP. Key Features Fully understand the certification exam content and exam objectives Consolidate your knowledge of all essential exam topics and key concepts Get realistic experience of answering exam-style questions Develop practical skills for everyday use Purchase of this book unlocks access to web-based exam prep resources including mock exams, flashcards, exam tips Book Description The GCP Professional Data Engineer certification validates the fundamental knowledge required to perform data engineering tasks and use GCP services to enhance data engineering processes and further your career in the data engineering/architecting field. This book is a best-in-class study guide that fully covers the GCP Professional Data Engineer exam objectives and helps you pass the exam first time. Complete with clear explanations, chapter review questions, realistic mock exams, and pragmatic solutions, this guide will help you master the core exam concepts and build the understanding you need to go into the exam with the skills and confidence to get the best result you can. With the help of relevant examples, you'll learn fundamental data engineering concepts such as data warehousing and data security. As you progress, you'll delve into the important domains of the exam, including data pipelining, data migration, and data processing. Unlike other study guides, this book contains logical reasoning behind the choice of correct answers based in scenarios and provide you with excellent tips regarding the optimal use of each service, and gives you everything you need to pass the exam and enhance your prospects in the data engineering field. What you will learn Create data solutions and pipelines in GCP Analyze and transform data into useful information Apply data engineering concepts to real scenarios Create secure, cost-effective, valuable GCP workloads Work in the GCP environment with industry best practices Who this book is for This book is for data engineers who want a reliable source for the key concepts and terms present in the most prestigious and highly-sought-after cloud-based data engineering certification. This book will help you improve your data engineering in GCP skills to give you a better chance at earning the GCP Professional Data Engineer Certification. You will already be familiar with the Google Cloud Platform, having either explored it (professionally or personally) for at least a year. You should also have some familiarity with basic data concepts (such as types of data and basic SQL knowledge).

The book simplifies the complexities of cloud transition and offers a clear, actionable roadmap for organizations moving from SAP BW or BW/4HANA to SAP Datasphere and SAP Analytics Cloud (as part of SAP Business Data Cloud), particularly in alignment with S/4HANA transformation. Whether you are assessing your current landscape, building a business case with ROI analysis, or creating a phased implementation strategy, this book delivers both technical and strategic guidance. It highlights short- and long-term planning considerations, outlines migration governance, and provides best practices for managing projects across hybrid SAP environments. From identifying platform gaps to facilitating stakeholder discussions, this book is an essential resource for anyone involved in the analytics modernization journey. You Will: [if !supportLists] · [endif] Learn how to assess your current SAP BW or BW/4HANA landscape and identify key migration drivers [if !supportLists] · [endif] Understand best practices for leveraging out-of-the-box cloud features and AI/ML capabilities [if !supportLists] · [endif] A step-by-step approach to planning and executing the move to SAP Business Data Cloud (Mainly SAP Datasphere and SAP Analytics Cloud) This book is for: SAP BW/BW4HANA Customers, SAP Consultants, Solution Architects and Enterprise Architects

Grow your software engineering discipline, incorporating and mastering design, development, testing, and deployment best practices examples in a realistic Python project structure. Key Features Understand what makes Software Engineering a discipline, distinct from basic programming Gain practical insight into updating, refactoring, and scaling an existing Python system Implement robust testing, CI/CD pipelines, and cloud-ready architecture decisions Book Description Software engineering is more than coding; it’s the strategic design and continuous improvement of systems that serve real-world needs. This newly updated second edition of Hands-On Software Engineering with Python expands on its foundational approach to help you grow into a senior or staff-level engineering role. Fully revised for today’s Python ecosystem, this edition includes updated tooling, practices, and architectural patterns. You’ll explore key changes across five minor Python versions, examine new features like dataclasses and type hinting, and evaluate modern tools such as Poetry, pytest, and GitHub Actions. A new chapter introduces high-performance computing in Python, and the entire development process is enhanced with cloud-readiness in mind. You’ll follow a complete redesign and refactor of a multi-tier system from the first edition, gaining insight into how software evolves—and what it takes to do that responsibly. From system modeling and SDLC phases to data persistence, testing, and CI/CD automation, each chapter builds your engineering mindset while updating your hands-on skills. By the end of this book, you'll have mastered modern Python software engineering practices and be equipped to revise and future-proof complex systems with confidence. What you will learn Distinguish software engineering from general programming Break down and apply each phase of the SDLC to Python systems Create system models to plan architecture before writing code Apply Agile, Scrum, and other modern development methodologies Use dataclasses, pydantic, and schemas for robust data modeling Set up CI/CD pipelines with GitHub Actions and cloud build tools Write and structure unit, integration, and end-to-end tests Evaluate and integrate tools like Poetry, pytest, and Docker Who this book is for This book is for Python developers with a basic grasp of software development who want to grow into senior or staff-level engineering roles. It’s ideal for professionals looking to deepen their understanding of software architecture, system modeling, testing strategies, and cloud-aware development. Familiarity with core Python programming is required, as the book focuses on applying engineering principles to maintain, extend, and modernize real-world systems.

Transform your data replication strategy into a competitive advantage with Oracle GoldenGate 23ai. This comprehensive guide delivers the practical knowledge DBAs and architects need to implement, optimize , and scale Oracle GoldenGate 23ai in production environments. Written by Oracle ACE Director Bobby Curtis, it blends deep technical expertise with real-world business insights from hundreds of implementations across manufacturing, financial services, and technology sectors. Beyond traditional replication, this book explores the groundbreaking capabilities that make GoldenGate 23ai essential for modern AI initiatives. Learn how to implement real-time vector replication for RAG systems, integrate with cloud platforms like GCP and Snowflake, and automate deployments using REST APIs and Python. Each chapter offers proven strategies to deliver measurable ROI while reducing operational risk. Whether you're upgrading from Classic GoldenGate , deploying your first cloud data pipeline, or building AI-ready data architectures, this book provides the strategic guidance and technical depth to succeed. With Bobby's signature direct approach, you'll avoid common pitfalls and implement best practices that scale with your business. What You Will Learn Master the microservices architecture and new capabilities of Oracle GoldenGate 23ai Implement secure, high-performance data replication across Oracle, PostgreSQL, and cloud databases Configure vector replication for AI and machine learning workloads, including RAG systems Design and build multi-master replication models with automatic conflict resolution Automate deployments and management using RESTful APIs and Python Optimize performance for sub-second replication lag in production environments Secure your replication environment with enterprise-grade features and compliance Upgrade from Classic to Microservices architecture with zero downtime Integrate with cloud platforms including OCI, GCP, AWS, and Azure Implement real-time data pipelines to BigQuery , Snowflake, and other cloud targets Navigate Oracle licensing models and optimize costs Who This Book Is For Database administrators, architects, and IT leaders working with Oracle GoldenGate —whether deploying for the first time, migrating from Classic architecture, or enabling AI-driven replication—will find actionable guidance on implementation, performance tuning, automation, and cloud integration. Covers unidirectional and multi-master replication and is packed with real-world use cases.

Leverage your smartphone and smartwatch for improved personal safety! Version 2.0, updated November 12, 2025 The digital and “real” worlds can both be scary places. The smartphone (and often smartwatch) you already carry with you can help reduce risks, deter theft, and mitigate violence. This book teaches you to secure your hardware, block abuse, automatically call emergency services, connect with others to ensure you arrive where and when you intended, detect stalking by compact trackers, and keep your ecosystem accounts from Apple, Google, and Microsoft secure. You don’t have to be reminded of the virtual and physical risks you face every day. Some of us are targeted more than others. Modern digital features built into mobile operating systems (and some computer operating systems) can help reduce our anxiety by putting more power in our hands to deter, deflect, block, and respond to abuse, threats, and emergencies. Keep Safe Using Mobile Tech looks at both digital threats, like online abuse and account hijacking, and ones in the physical world, like being stalked through Bluetooth trackers, facing domestic violence, or being in a car crash. The book principally covers the iPhone, Apple Watch, Android devices, and Wear OS watches. It also covers more limited but useful features available on the iPad and on computers running macOS or Windows. This second edition incorporates the massive number of new safety features Google added since October 2024 to the Android operating system, some particular to Google Pixel phones and smartwatches, and improved blocking, filtering, and screening added to Apple’s iOS 26 and related operating system updates in fall 2025. This book explores many techniques to help:

Learn how to harden your Apple Account, Google Account, and Microsoft Account beyond just a password or a text-message token. Discover filtering and blocking tools from Apple and Google that can prevent abusive, fraudulent, and phishing messages and calls from reaching you. Block seeing unwanted sensitive images on your iPhone, iPad, Mac, Apple Watch, or Android phone—and help your kids receive advice on how not to send them. Turn on tracking on your Apple, Google, and Microsoft devices, and use it to recover or erase stolen hardware. Keep your cloud-archived messages from leaking to attackers. Screen calls with an automated assistant so that you know who wants you before picking up and without sending to voicemail. Lock down your devices to keep thieves and other personal invaders from accessing them. Prepare for emergencies by setting up medical information on your mobile devices. Let a supported smartphone or smartwatch recognize when you’re in a car crash or have taken a hard fall and call emergency services for you (and text your emergency contacts) if you can’t respond. Keep track of heart anomalies through smartwatch alerts and tests on your Apple Watch and many Android Wear smartwatches. Tell others where or when you expect to check in with them again, and let your smartphone alert them if you don’t with your Apple iPhone or Android phone. Deter stalking from tiny Bluetooth trackers. Protect your devices and accounts against access from domestic assailants. Block thieves who steal your phone—potentially threatening you or attacking you in person—from gaining access to the rest of your digital life.

A hands-on technical and industry roadmap for aspiring data engineers In Data Engineering for Beginners, big data expert Chisom Nwokwu delivers a beginner-friendly handbook for everyone interested in the fundamentals of data engineering. Whether you're interested in starting a rewarding, new career as a data analyst, data engineer, or data scientist, or seeking to expand your skillset in an existing engineering role, Nwokwu offers the technical and industry knowledge you need to succeed. The book explains: Database fundamentals, including relational and noSQL databases Data warehouses and data lakes Data pipelines, including info about batch and stream processing Data quality dimensions Data security principles, including data encryption Data governance principles and data framework Big data and distributed systems concepts Data engineering on the cloud Essential skills and tools for data engineering interviews and jobs Data Engineering for Beginners offers an easy-to-read roadmap on a seemingly complicated and intimidating subject. It addresses the topics most likely to cause a beginning data engineer to stumble, clearly explaining key concepts in an accessible way. You'll also find: A comprehensive glossary of data engineering terms Common and practical career paths in the data engineering industry An introduction to key cloud technologies and services you may encounter early in your data engineering career Perfect for practicing and aspiring data analysts, data scientists, and data engineers, Data Engineering for Beginners is an effective and reliable starting point for learning an in-demand skill. It's a powerful resource for everyone hoping to expand their data engineering Skillset and upskill in the big data era.

Unlock the full financial potential of your Snowflake environment. Learn how to cut costs, boost performance, and take control of your cloud data spend with FinOps for Snowflake—your essential guide to implementing a smart, automated, and Snowflake-optimized FinOps strategy. In today’s data-driven world, financial optimization on platforms like Snowflake is more critical than ever. Whether you're just beginning your FinOps journey or refining mature practices, this book provides a practical roadmap to align Snowflake usage with business goals, reduce costs, and improve performance—without compromising agility. Grounded in real-world case studies and packed with actionable strategies, FinOps for Snowflake shows how leading organizations are transforming their environments through automation, governance, and cost intelligence. You'll learn how to apply proven techniques for architecture tuning, workload and storage efficiency, and performance optimization—empowering you to make smarter, data-driven decisions. What You Will Learn Master FinOps principles tailored for Snowflake’s architecture and pricing model Enable collaboration across finance, engineering, and business teams Deliver real-time cost insights for smarter decision-making Optimize compute, storage, and Snowflake AI and ML services for efficiency Leverage Snowflake Cortex AI and Adoptive Warehouse/Compute for intelligent cost governance Apply proven strategies to achieve operational excellence and measurable savings Who this Book is For Data professionals, cloud engineers, FinOps practitioners, and finance teams seeking to improve cost visibility, operational efficiency, and financial accountability in Snowflake environments.

Master the art of data transformation with the second edition of this trusted guide to dbt. Building on the foundation of the first edition, this updated volume offers a deeper, more comprehensive exploration of dbt’s capabilities—whether you're new to the tool or looking to sharpen your skills. It dives into the latest features and techniques, equipping you with the tools to create scalable, maintainable, and production-ready data transformation pipelines. Unlocking dbt, Second Edition introduces key advancements, including the semantic layer, which allows you to define and manage metrics at scale, and dbt Mesh, empowering organizations to orchestrate decentralized data workflows with confidence. You’ll also explore more advanced testing capabilities, expanded CI/CD and deployment strategies, and enhancements in documentation—such as the newly introduced dbt Catalog. As in the first edition, you’ll learn how to harness dbt’s power to transform raw data into actionable insights, while incorporating software engineering best practices like code reusability, version control, and automated testing. From configuring projects with the dbt Platform or open source dbt to mastering advanced transformations using SQL and Jinja, this book provides everything you need to tackle real-world challenges effectively. What You Will Learn Understand dbt and its role in the modern data stack Set up projects using both the cloud-hosted dbt Platform and open source project Connect dbt projects to cloud data warehouses Build scalable models in SQL and Python Configure development, testing, and production environments Capture reusable logic with Jinja macros Incorporate version control with your data transformation code Seamlessly connect your projects using dbt Mesh Build and manage a semantic layer using dbt Deploy dbt using CI/CD best practices Who This Book Is For Current and aspiring data professionals, including architects, developers, analysts, engineers, data scientists, and consultants who are beginning the journey of using dbt as part of their data pipeline’s transformation layer. Readers should have a foundational knowledge of writing basic SQL statements, development best practices, and working with data in an analytical context such as a data warehouse.

Follow the cloud journey of a fictional company Nimbus Airlines and the process it goes through to modernize its SAP systems. This book provides a detailed guide for those looking to transition their SAP systems to the cloud using Amazon Web Services (AWS). Through the lens of various characters, the book is structured in three parts — starting with an introduction to SAP and AWS fundamentals, followed by technical architecture insights, and concluding with migration strategies and case studies, the book covers technical aspects of modernizing SAP with AWS. You’ll review the partnership between SAP and AWS, highlighted by their long-standing collaboration and shared innovations. Then design an AWS architecture tailored for SAP workloads, including high availability, disaster recovery, and operations automation. The book concludes with a tour of the migration process, offering various strategies, tools, and frameworks reinforced with real-world customer case studies that showcase successful SAP migrations to AWS. Modernizing SAP with AWS equips business leaders and technical architects with the knowledge to leverage AWS for their SAP systems, ensuring a smooth transition and unlocking new opportunities for innovation. What You Will Learn Understand the fundamentals of AWS and its key components, including computing, storage, networking, and microservices, for SAP systems. Explore the technical partnership between SAP and AWS, learning how their collaboration drives innovation and delivers business value. Design an optimized AWS architecture for SAP workloads, focusing on high availability, disaster recovery, and operations automation. Discover innovative ways to enhance and extend SAP functionality using AWS tools for better system performance and automation. Who This Book Is For SAP professionals and consultants interested in learning how AWS can enhance SAP performance, security, and automation. Cloud engineers and developers involved in SAP migration projects, looking for best practices and real-world case studies for successful implementation. Enterprise architects seeking to design optimized, scalable, and secure SAP infrastructure on AWS. CIOs, CTOs, and IT managers aiming to modernize SAP systems and unlock innovation through cloud technology.

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.

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.

This book is your guide to the modern market of data analytics platforms and the benefits of using Snowflake, the data warehouse built for the cloud. As organizations increasingly rely on modern cloud data platforms, the core of any analytics framework—the data warehouse—is more important than ever. This updated 2nd edition ensures you are ready to make the most of the industry’s leading data warehouse. This book will onboard you to Snowflake and present best practices for deploying and using the Snowflake data warehouse. The book also covers modern analytics architecture, integration with leading analytics software such as Matillion ETL, Tableau, and Databricks, and migration scenarios for on-premises legacy data warehouses. This new edition includes expanded coverage of SnowPark for developing complex data applications, an introduction to managing large datasets with Apache Iceberg tables, and instructions for creating interactive data applications using Streamlit, ensuring readers are equipped with the latest advancements in Snowflake's capabilities. What You Will Learn Master key functionalities of Snowflake Set up security and access with cluster Bulk load data into Snowflake using the COPY command Migrate from a legacy data warehouse to Snowflake Integrate the Snowflake data platform with modern business intelligence (BI) and data integration tools Manage large datasets with Apache Iceberg Tables Implement continuous data loading with Snowpipe and Dynamic Tables Who This Book Is For Data professionals, business analysts, IT administrators, and existing or potential Snowflake users

The SAP S/4HANA Handbook for EPC Projects equips you with the knowledge and insights needed to successfully manage and execute complex Engineering, Procurement, and Construction (EPC) projects using the power of SAP S/4HANA. Building upon your existing knowledge of SAP solutions, this handbook provides advanced insights into EPC project management and addresses the operational challenges unique to the Engineering, Construction and Operations (EC&O) industry by connecting business processes with relevant SAP solutions. It is an essential guide enabling you to gain a deeper understanding of optimizing your project management capabilities using SAP S/4HANA. Whether you are an SAP Solution Architect in Finance, Human Resources, or Supply Chain Management, or a project manager in the EC&O industry, this book will help you understand how projects can be managed with SAP. We begin by examining the world of EPC, EPC/M (Engineering, Procurement, Construction, and Management), and ETO (Engineer-To-Order) projects.Looking at detailed planning, controlling, and execution solutions of EPC projects with S/4HANA Project System, CPM (Commercial Project Management), PPM (Project & Portfolio Management), S/4HANA Add-ons, SAP Cloud Solutions, and to integrate these with other engineering and project management software such as Tekla and Primavera through SAP BTP (Business Technology Platform). You will follow a construction company secure an EPC contract of a refinery upgrade project and demonstrates how SAP is used at every step of the way, from bidding to project closure. Through real-world use-cases, supported by tables and visual aids, you will find the practical solutions offered by SAP S/4HANA. The SAP S/4HANA Handbook for EPC Projects is the ultimate resource bridging theory with practical applications, offering a framework to navigate the complexities of modern project management in the EC&O industry. You Will Learn To: [if !supportLists] Understand project management processes with business use cases and their application in SAP Apply detailed planning, scheduling, resource and management strategies, as well as for risk and claim managmement in large-scale projects. [if !supportLists] Master project procurement, ETO manufacturing for projects, product and service quality management and the handling of project materials, tools and equipment. [if !supportLists] Manage the design and creation of documentation and oversee change management in EPC projects. This Book is For: Project and Portfolio Managers, SAP Solution Architects and other SAP partners looking for hands-on solution s for the EC&O industry. Engineering and Construction Contractors, Engineering Consultants, and Project Management Services companies seeking business transformation with SAP tools and practices

Deliver flexible, scalable, and high-performance data storage that's perfect for AI and other modern applications with MongoDB 8.0 and MongoDB Atlas multi-cloud data platform. In MongoDB 8.0 in Action, Third Edition you'll find comprehensive coverage of the latest version of MongoDB 8.0 and the MongoDB Atlas multi-cloud data platform. Learn to utilize MongoDB’s flexible schema design for data modeling, scale applications effectively using advanced sharding features, integrate full-text and vector-based semantic search, and more. This totally revised new edition delivers engaging hands-on tutorials and examples that put MongoDB into action! In MongoDB 8.0 in Action, Third Edition you'll: Master new features in MongoDB 8.0 Create your first, free Atlas cluster using the Atlas CLI Design scalable NoSQL databases with effective data modeling techniques Master Vector Search for building GenAI-driven applications Utilize advanced search capabilities in MongoDB Atlas, including full-text search Build Event-Driven Applications with Atlas Stream Processing Deploy and manage MongoDB Atlas clusters both locally and in the cloud using the Atlas CLI Leverage the Atlas SQL interface for familiar SQL querying Use MongoDB Atlas Online Archive for efficient data management Establish robust security practices including encryption Master backup and restore strategies Optimize database performance and identify slow queries MongoDB 8.0 in Action, Third Edition offers a clear, easy-to-understand introduction to everything in MongoDB 8.0 and MongoDB Atlas—including new advanced features such as embedded config servers in sharded clusters, or moving an unsharded collection to a different shard. The book also covers Atlas stream processing, full text search, and vector search capabilities for generative AI applications. Each chapter is packed with tips, tricks, and practical examples you can quickly apply to your projects, whether you're brand new to MongoDB or looking to get up to speed with the latest version. About the Technology MongoDB is the database of choice for storing structured, semi-structured, and unstructured data like business documents and other text and image files. MongoDB 8.0 introduces a range of exciting new features—from sharding improvements that simplify the management of distributed data, to performance enhancements that stay resilient under heavy workloads. Plus, MongoDB Atlas brings vector search and full-text search features that support AI-powered applications. About the Book MongoDB 8.0 in Action, Third Edition you’ll learn how to take advantage of all the new features of MongoDB 8.0, including the powerful MongoDB Atlas multi-cloud data platform. You’ll start with the basics of setting up and managing a document database. Then, you’ll learn how to use MongoDB for AI-driven applications, implement advanced stream processing, and optimize performance with improved indexing and query handling. Hands-on projects like creating a RAG-based chatbot and building an aggregation pipeline mean you’ll really put MongoDB into action! What's Inside The new features in MongoDB 8.0 Get familiar with MongoDB’s Atlas cloud platform Utilizing sharding enhancements Using vector-based search technologies Full-text search capabilities for efficient text indexing and querying About the Reader For developers and DBAs of all levels. No prior experience with MongoDB required. About the Author Arek Borucki is a MongoDB Champion, certified MongoDB and MongoDB Atlas administrator with expertise in distributed systems, NoSQL databases, and Kubernetes. Quotes An excellent resource with real-world examples and best practices to design, optimize, and scale modern applications. - Advait Patel, Broadcom Essential MongoDB resource. Covers new features such as full-text search, vector search, AI, and RAG applications. - Juan Roy, Credit Suisse Reflects author’s practical experience and clear teaching style. It’s packed with real-world examples and up-to-date insights. - Rajesh Nair, MongoDB Champion & community leader This book will definitely make you a MongoDB star! - Vinicios Wentz, JP Morgan & Chase Co.

Dive into building applications that combine the power of Large Language Models (LLMs) with Neo4j knowledge graphs, Haystack, and Spring AI to deliver intelligent, data-driven recommendations and search outcomes. This book provides actionable insights and techniques to create scalable, robust solutions by leveraging the best-in-class frameworks and a real-world project-oriented approach. What this Book will help me do Understand how to use Neo4j to build knowledge graphs integrated with LLMs for enhanced data insights. Develop skills in creating intelligent search functionalities by combining Haystack and vector-based graph techniques. Learn to design and implement recommendation systems using LangChain4j and Spring AI frameworks. Acquire the ability to optimize graph data architectures for LLM-driven applications. Gain proficiency in deploying and managing applications on platforms like Google Cloud for scalability. Author(s) Ravindranatha Anthapu, a Principal Consultant at Neo4j, and Siddhant Agarwal, a Google Developer Expert in Generative AI, bring together their vast experience to offer practical implementations and cutting-edge techniques in this book. Their combined expertise in Neo4j, graph technology, and real-world AI applications makes them authoritative voices in the field. Who is it for? Designed for database developers and data scientists, this book caters to professionals aiming to leverage the transformational capabilities of knowledge graphs alongside LLMs. Readers should have a working knowledge of Python and Java as well as familiarity with Neo4j and the Cypher query language. If you're looking to enhance search or recommendation functionalities through state-of-the-art AI integrations, this book is for you.

This concise yet comprehensive guide shows developers and architects how to tackle data integration challenges with MuleSoft. Authors Pooja Kamath and Diane Kesler take you through the process necessary to build robust and scalable integration solutions step-by-step. Supported by real-world use cases, Building Integrations with MuleSoft teaches you to identify and resolve performance bottlenecks, handle errors, and ensure the reliability and scalability of your integration solutions. You'll explore MuleSoft's robust set of connectors and their components, and use them to connect to systems and applications from legacy databases to cloud services. Ask the right questions to determine your use case, define requirements, decide on reuse versus rebuild, and create sequence and context diagrams Master tools like the Anypoint Platform, Anypoint Studio, Code Builder, GitHub, and Maven Design APIs with RAML and OAS and craft effective requests and responses Write MUnit tests, validate DataWeave expressions, and use Postman Collections Deploy Mule applications to CloudHub, use API Manager to create API proxies, and secure APIs with Mule OAuth 2.0 Learn message orchestration techniques for routers, transactions, error handling, For Each, Parallel For Each, and batch processing