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| gcp architecture diagram examples: Data Analytics with Google Cloud Platform Murari Ramuka, 2019-12-16 Step-by-step guide to different data movement and processing techniques, using Google Cloud Platform Services DESCRIPTION Modern businesses are awash with data, making data-driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with enough knowledge of Cloud Computing in conjunction with Google Cloud Data platform to succeed in the role of a Cloud data expert. The current market is trending towards the latest cloud technologies, which is the need of the hour. Google being the pioneer, is dominating this space with the right set of cloud services being offered as part of GCP (Google Cloud Platform). At this juncture, this book will be very vital and will cover all the services that are being offered by GCP, putting emphasis on Data services. This book starts with sophisticated knowledge on Cloud Computing. It also explains different types of data services/technology and machine learning algorithm/Pre-Trained API through real-business problems, which are built on the Google Cloud Platform (GCP). With some of the latest business examples and hands-on guide, this book will enable the developers entering the data analytics fields to implement an end-to-end data pipeline, using GCP Data services. Through the course of the book, you will come across multiple industry-wise use cases, like Building Datawarehouse using Big Query, a sample real-time data analytics solution on machine learning and Artificial Intelligence that helped with the business decision, by employing a variety of data science approaches on Google Cloud environment. Whether yourÊbusinessÊis at the early stage of cloud implementation in its journey or well on its way to digital transformation,ÊGoogle Cloud'sÊsolutions and technologies will always help chart a path to success. This book can be used to develop the GCP concepts in an easy way. It contains many examples showcasing the implementation of a GCP service. It enables the learning of the basic and advance concepts of Google Cloud Data Platform. This book is divided into 7 chapters and provides a detailed description of the core concepts of each of the Data services offered by Google Cloud. KEY FEATURES Learn the basic concept of Cloud Computing along with different Cloud service provides with their supported Models (IaaS/PaaS/SaaS) Learn the basics of Compute Engine, App Engine, Container Engine, Project and Billing setup in the Google Cloud Platform Learn how and when to use Cloud DataFlow, Cloud DataProc and Cloud DataPrepÊ Build real-time data pipeline to support real-time analytics using Pub/Sub messaging service Setting up a fully managed GCP Big Data Cluster using Cloud DataProc for runningÊApache SparkÊandÊApache HadoopÊclusters in a simpler, more cost-efficient manner Learn how to use Cloud Data Studio for visualizing the data on top of Big Query Implement and understand real-world business scenarios for Machine Learning, Data Pipeline Engineering WHAT WILL YOU LEARN By the end of the book, you will have come across different data services and platforms offered by Google Cloud, and how those services/features can be enabled to serve business needs. You will also see a few case studies to put your knowledge to practice and solve business problems such as building a real-time streaming pipeline engine, Scalable Data Warehouse on Cloud, fully managed Hadoop cluster on Cloud and enabling TensorFlow/Machine Learning APIÕs to support real-life business problems. Remember to practice additional examples to master these techniques. WHO IS THIS BOOK FOR This book is for professionals as well as graduates who want to build a career in Google Cloud data analytics technologies. While no prior knowledge of Cloud Computing or related technologies is assumed, it will be helpful to have some data background and experience. One stop shop for those who wish to get an initial to advance understanding of the GCP data platform. The target audience will be data engineers/professionals who are new, as well as those who are acquainted with the tools and techniques related to cloud and data space.ÊÊ _Ê Ê Ê Individuals who have basic data understanding (i.e. Data and cloud) and have done some work in the field ofÊ data analytics, can refer/use this book to master their knowledge/understanding. _Ê Ê Ê The highlight of this book is that it will start with theÊ basic cloud computing fundamentals and will move on to cover the advance concepts on GCP cloud data analytics and hence can be referred across multiple different levels of audiences.Ê Table of Contents 1. GCP Overview and Architecture 2. Data Storage in GCPÊ 3. Data Processing in GCP with Pub/Sub and DataflowÊ 4. Data Processing in GCP with DataPrep and Dataflow 5. Big Query and Data Studio 6. Machine Learning with GCP 7. Sample Use cases and Examples |
| gcp architecture diagram examples: Security Architecture for Hybrid Cloud Mark Buckwell, Stefaan Van daele, Carsten Horst, 2024-07-25 As the transformation to hybrid multicloud accelerates, businesses require a structured approach to securing their workloads. Adopting zero trust principles demands a systematic set of practices to deliver secure solutions. Regulated businesses, in particular, demand rigor in the architectural process to ensure the effectiveness of security controls and continued protection. This book provides the first comprehensive method for hybrid multicloud security, integrating proven architectural techniques to deliver a comprehensive end-to-end security method with compliance, threat modeling, and zero trust practices. This method ensures repeatability and consistency in the development of secure solution architectures. Architects will learn how to effectively identify threats and implement countermeasures through a combination of techniques, work products, and a demonstrative case study to reinforce learning. You'll examine: The importance of developing a solution architecture that integrates security for clear communication Roles that security architects perform and how the techniques relate to nonsecurity subject matter experts How security solution architecture is related to design thinking, enterprise security architecture, and engineering How architects can integrate security into a solution architecture for applications and infrastructure using a consistent end-to-end set of practices How to apply architectural thinking to the development of new security solutions About the authors Mark Buckwell is a cloud security architect at IBM with 30 years of information security experience. Carsten Horst with more than 20 years of experience in Cybersecurity is a certified security architect and Associate Partner at IBM. Stefaan Van daele has 25 years experience in Cybersecurity and is a Level 3 certified security architect at IBM. |
| gcp architecture diagram examples: Data Engineering with Google Cloud Platform Adi Wijaya, 2022-03-31 Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book. |
| gcp architecture diagram examples: Architecting Google Cloud Solutions Victor Dantas, 2021-05-14 Achieve your business goals and build highly available, scalable, and secure cloud infrastructure by designing robust and cost-effective solutions as a Google Cloud Architect. Key FeaturesGain hands-on experience in designing and managing high-performance cloud solutionsLeverage Google Cloud Platform to optimize technical and business processes using cutting-edge technologies and servicesUse Google Cloud Big Data, AI, and ML services to design scalable and intelligent data solutionsBook Description Google has been one of the top players in the public cloud domain thanks to its agility and performance capabilities. This book will help you design, develop, and manage robust, secure, and dynamic solutions to successfully meet your business needs. You'll learn how to plan and design network, compute, storage, and big data systems that incorporate security and compliance from the ground up. The chapters will cover simple to complex use cases for devising solutions to business problems, before focusing on how to leverage Google Cloud's Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) capabilities for designing modern no-operations platforms. Throughout this book, you'll discover how to design for scalability, resiliency, and high availability. Later, you'll find out how to use Google Cloud to design modern applications using microservices architecture, automation, and Infrastructure-as-Code (IaC) practices. The concluding chapters then demonstrate how to apply machine learning and artificial intelligence (AI) to derive insights from your data. Finally, you will discover best practices for operating and monitoring your cloud solutions, as well as performing troubleshooting and quality assurance. By the end of this Google Cloud book, you'll be able to design robust enterprise-grade solutions using Google Cloud Platform. What you will learnGet to grips with compute, storage, networking, data analytics, and pricingDiscover delivery models such as IaaS, PaaS, and SaaSExplore the underlying technologies and economics of cloud computingDesign for scalability, business continuity, observability, and resiliencySecure Google Cloud solutions and ensure complianceUnderstand operational best practices and learn how to architect a monitoring solutionGain insights into modern application design with Google CloudLeverage big data, machine learning, and AI with Google CloudWho this book is for This book is for cloud architects who are responsible for designing and managing cloud solutions with GCP. You'll also find the book useful if you're a system engineer or enterprise architect looking to learn how to design solutions with Google Cloud. Moreover, cloud architects who already have experience with other cloud providers and are now beginning to work with Google Cloud will benefit from the book. Although an intermediate-level understanding of cloud computing and distributed apps is required, prior experience of working in the public and hybrid cloud domain is not mandatory. |
| gcp architecture diagram examples: Multi-Cloud Architecture and Governance Jeroen Mulder, 2020-12-11 A comprehensive guide to architecting, managing, implementing, and controlling multi-cloud environments Key Features Deliver robust multi-cloud environments and improve your business productivity Stay in control of the cost, governance, development, security, and continuous improvement of your multi-cloud solution Integrate different solutions, principles, and practices into one multi-cloud foundation Book DescriptionMulti-cloud has emerged as one of the top cloud computing trends, with businesses wanting to reduce their reliance on only one vendor. But when organizations shift to multiple cloud services without a clear strategy, they may face certain difficulties, in terms of how to stay in control, how to keep all the different components secure, and how to execute the cross-cloud development of applications. This book combines best practices from different cloud adoption frameworks to help you find solutions to these problems. With step-by-step explanations of essential concepts and practical examples, you’ll begin by planning the foundation, creating the architecture, designing the governance model, and implementing tools, processes, and technologies to manage multi-cloud environments. You’ll then discover how to design workload environments using different cloud propositions, understand how to optimize the use of these cloud technologies, and automate and monitor the environments. As you advance, you’ll delve into multi-cloud governance, defining clear demarcation models and management processes. Finally, you’ll learn about managing identities in multi-cloud: who’s doing what, why, when, and where. By the end of this book, you’ll be able to create, implement, and manage multi-cloud architectures with confidenceWhat you will learn Get to grips with the core functions of multiple cloud platforms Deploy, automate, and secure different cloud solutions Design network strategy and get to grips with identity and access management for multi-cloud Design a landing zone spanning multiple cloud platforms Use automation, monitoring, and management tools for multi-cloud Understand multi-cloud management with the principles of BaseOps, FinOps, SecOps, and DevOps Define multi-cloud security policies and use cloud security tools Test, integrate, deploy, and release using multi-cloud CI/CD pipelines Who this book is for This book is for architects and lead engineers involved in architecting multi-cloud environments, with a focus on getting governance right to stay in control of developments in multi-cloud. Basic knowledge of different cloud platforms (Azure, AWS, GCP, VMWare, and OpenStack) and understanding of IT governance is necessary. |
| gcp architecture diagram examples: Enterprise DevOps for Architects Jeroen Mulder, 2021-11-11 An architect's guide to designing, implementing, and integrating DevOps in the enterprise Key FeaturesDesign a DevOps architecture that is aligned with the overall enterprise architectureDesign systems that are ready for AIOps and make the move toward NoOpsArchitect and implement DevSecOps pipelines, securing the DevOps enterpriseBook Description Digital transformation is the new paradigm in enterprises, but the big question remains: is the enterprise ready for transformation using native technology embedded in Agile/DevOps? With this book, you'll see how to design, implement, and integrate DevOps in the enterprise architecture while keeping the Ops team on board and remaining resilient. The focus of the book is not to introduce the hundreds of different tools that are available for implementing DevOps, but instead to show you how to create a successful DevOps architecture. This book provides an architectural overview of DevOps, AIOps, and DevSecOps – the three domains that drive and accelerate digital transformation. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this DevOps book will help you to successfully integrate DevOps into enterprise architecture. You'll learn what AIOps is and what value it can bring to an enterprise. Lastly, you will learn how to integrate security principles such as zero-trust and industry security frameworks into DevOps with DevSecOps. By the end of this DevOps book, you'll be able to develop robust DevOps architectures, know which toolsets you can use for your DevOps implementation, and have a deeper understanding of next-level DevOps by implementing Site Reliability Engineering (SRE). What you will learnCreate DevOps architecture and integrate it with the enterprise architectureDiscover how DevOps can add value to the quality of IT deliveryExplore strategies to scale DevOps for an enterpriseArchitect SRE for an enterprise as next-level DevOpsUnderstand AIOps and what value it can bring to an enterpriseCreate your AIOps architecture and integrate it into DevOpsCreate your DevSecOps architecture and integrate it with the existing DevOps setupApply zero-trust principles and industry security frameworks to DevOpsWho this book is for This book is for enterprise architects and consultants who want to design DevOps systems for the enterprise. It provides an architectural overview of DevOps, AIOps, and DevSecOps. If you're looking to learn about the implementation of various tools within the DevOps toolchain in detail, this book is not for you. |
| gcp architecture diagram examples: Cloud Penetration Testing Kim Crawley, 2023-11-24 Get to grips with cloud exploits, learn the fundamentals of cloud security, and secure your organization's network by pentesting AWS, Azure, and GCP effectively Key Features Discover how enterprises use AWS, Azure, and GCP as well as the applications and services unique to each platform Understand the key principles of successful pentesting and its application to cloud networks, DevOps, and containerized networks (Docker and Kubernetes) Get acquainted with the penetration testing tools and security measures specific to each platform Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWith AWS, Azure, and GCP gaining prominence, understanding their unique features, ecosystems, and penetration testing protocols has become an indispensable skill, which is precisely what this pentesting guide for cloud platforms will help you achieve. As you navigate through the chapters, you’ll explore the intricacies of cloud security testing and gain valuable insights into how pentesters evaluate cloud environments effectively. In addition to its coverage of these cloud platforms, the book also guides you through modern methodologies for testing containerization technologies such as Docker and Kubernetes, which are fast becoming staples in the cloud ecosystem. Additionally, it places extended focus on penetration testing AWS, Azure, and GCP through serverless applications and specialized tools. These sections will equip you with the tactics and tools necessary to exploit vulnerabilities specific to serverless architecture, thus providing a more rounded skill set. By the end of this cloud security book, you’ll not only have a comprehensive understanding of the standard approaches to cloud penetration testing but will also be proficient in identifying and mitigating vulnerabilities that are unique to cloud environments.What you will learn Familiarize yourself with the evolution of cloud networks Navigate and secure complex environments that use more than one cloud service Conduct vulnerability assessments to identify weak points in cloud configurations Secure your cloud infrastructure by learning about common cyber attack techniques Explore various strategies to successfully counter complex cloud attacks Delve into the most common AWS, Azure, and GCP services and their applications for businesses Understand the collaboration between red teamers, cloud administrators, and other stakeholders for cloud pentesting Who this book is for This book is for aspiring Penetration Testers, and the Penetration Testers seeking specialized skills for leading cloud platforms—AWS, Azure, and GCP. Those working in defensive security roles will also find this book useful to extend their cloud security skills. |
| gcp architecture diagram examples: Journey to Become a Google Cloud Machine Learning Engineer Dr. Logan Song, 2022-09-20 Prepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skills Key FeaturesA comprehensive yet easy-to-follow Google Cloud machine learning study guideExplore full-spectrum and step-by-step practice examples to develop hands-on skillsRead through and learn from in-depth discussions of Google ML certification exam questionsBook Description This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate. What you will learnProvision Google Cloud services related to data science and machine learningProgram with the Python programming language and data science librariesUnderstand machine learning concepts and model development processesExplore deep learning concepts and neural networksBuild, train, and deploy ML models with Google BigQuery ML, Keras, and Google Cloud Vertex AIDiscover the Google Cloud ML Application Programming Interface (API)Prepare to achieve Google Cloud Professional Machine Learning Engineer certificationWho this book is for Anyone from the cloud computing, data analytics, and machine learning domains, such as cloud engineers, data scientists, data engineers, ML practitioners, and engineers, will be able to acquire the knowledge and skills and achieve the Google Cloud professional ML Engineer certification with this study guide. Basic knowledge of Google Cloud Platform and Python programming is required to get the most out of this book. |
| gcp architecture diagram examples: Solutions Architect's Handbook Saurabh Shrivastava, Neelanjali Srivastav, 2022-01-17 Third edition out now with coverage on Generative AI, clean architecture, edge computing, and more Key Features Turn business needs into end-to-end technical architectures with this practical guide Assess and overcome various challenges while updating or modernizing legacy applications Future-proof your architecture with IoT, machine learning, and quantum computing Book DescriptionBecoming a solutions architect requires a hands-on approach, and this edition of the Solutions Architect's Handbook brings exactly that. This handbook will teach you how to create robust, scalable, and fault-tolerant solutions and next-generation architecture designs in a cloud environment. It will also help you build effective product strategies for your business and implement them from start to finish. This new edition features additional chapters on disruptive technologies, such as Internet of Things (IoT), quantum computing, data engineering, and machine learning. It also includes updated discussions on cloud-native architecture, blockchain data storage, and mainframe modernization with public cloud. The Solutions Architect's Handbook provides an understanding of solution architecture and how it fits into an agile enterprise environment. It will take you through the journey of solution architecture design by providing detailed knowledge of design pillars, advanced design patterns, anti-patterns, and the cloud-native aspects of modern software design. By the end of this handbook, you'll have learned the techniques needed to create efficient architecture designs that meet your business requirements.What you will learn Explore the various roles of a solutions architect in the enterprise landscape Implement key design principles and patterns to build high-performance cost-effective solutions Choose the best strategies to secure your architectures and increase their availability Modernize legacy applications with the help of cloud integration Understand how big data processing, machine learning, and IoT fit into modern architecture Integrate a DevOps mindset to promote collaboration, increase operational efficiency, and streamline production Who this book is for This book is for software developers, system engineers, DevOps engineers, architects, and team leaders who already work in the IT industry and aspire to become solutions architect professionals. Existing solutions architects who want to expand their skillset or get a better understanding of new technologies will also learn valuable new skills. To get started, you'll need a good understanding of the real-world software development process and general programming experience in any language. |
| gcp architecture diagram examples: Developing Blockchain Solutions in the Cloud Stefano Tempesta, Michael John Peña, 2024-04-26 Learn how to implement, deploy, and manage blockchain solutions across AWS, Azure, and GCP with the help of hands-on labs and real-world use cases Key Features Learn architecture design patterns and access code samples for building Web3 apps in the cloud Master the latest tools and cloud technologies for integrating DevOps in blockchain applications Strengthen your understanding of cloud-native blockchain through real-world use cases and best practices Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAs Web3 technologies continue to gain momentum across industries, businesses are looking for new ways to leverage the benefits of Web3 and stay at the forefront of technological innovation. This comprehensive guide offers an in-depth exploration of cloud-native blockchain fundamentals, providing valuable insights into the benefits and challenges of deploying these technologies in the cloud. From foundational concepts to advanced techniques, the book covers everything you need to know about developing and deploying secure, scalable blockchain solutions on AWS, Azure, and GCP. Through hands-on tutorials and projects, you’ll explore the latest tools, technologies, real-world use cases, and best practices to expand your understanding of the field’s complexities and opportunities. This book ensures easy comprehension through practical examples and access to source code on GitHub. As you advance, you’ll master platform selection and apply your newfound knowledge to tackle complex problems and deliver innovative cloud-native blockchain solutions tailored for your specific needs. By the end of this book, you’ll have a deep understanding of cloud-native blockchain deployment and implementation, and you’ll be equipped with the skills and knowledge to build secure and scalable solutions.What you will learn Discover the benefits and challenges of deploying Web3 solutions in the cloud Deploy secure and scalable blockchain networks leveraging AWS, Azure, and GCP resources Follow step-by-step tutorials and code samples to build Web3 solutions in the cloud Use hosted Kubernetes platforms, such as EKS, AKS, and GKE, for custom blockchains Compare the blockchain capabilities and offerings of AWS, Azure, and Google Cloud Familiarize yourself with the tools and techniques for automating DevOps practices tailored to Web3 apps Who this book is for The book is for cloud developers and DevOps engineers who want to leverage blockchain technologies in their cloud-native solutions. Whether you’re an IT professional deploying and maintaining Web3 solutions in the enterprise or in public settings, or a business leader evaluating blockchain's potential, this resource is invaluable. Entrepreneurs, students, academics, and hobbyists exploring the latest Web3 development trends will also benefit from this book. Prior knowledge of cloud computing and blockchain concepts is recommended to make the best use of the expert insights, hands-on tutorials, and real-world use cases presented. |
| gcp architecture diagram examples: Effective .NET Memory Management Trevoir Williams, 2024-07-30 Master optimal memory management techniques in .NET Core, from understanding memory allocation to implementing advanced garbage collection strategies Key Features Discover tools and strategies to build efficient, scalable applications Implement .NET memory management techniques to effectively boost your application’s performance Uncover practical methods for troubleshooting memory leaks and diagnosing performance bottlenecks Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn today’s software development landscape, efficient memory management is crucial for ensuring application performance and scalability. Effective .NET Memory Management addresses this need by explaining the intricacies of memory utilization within .NET Core apps, from fundamental concepts to advanced optimization techniques. Starting with an overview of memory management basics, you’ll quickly go through .NET’s garbage collection system. You’ll grasp the mechanics of memory allocation and gain insights into the distinctions between stack and heap memory and the nuances of value types and reference types. Building on this foundation, this book will help you apply practical strategies to address real-world app demands, spanning profiling memory usage, spotting memory leaks, and diagnosing performance bottlenecks, through clear explanations and hands-on examples. This book goes beyond theory, detailing actionable techniques to optimize data structures, minimize memory fragmentation, and streamline memory access in scenarios involving multithreading and asynchronous programming for creating responsive and resource-efficient apps that can scale without sacrificing performance. By the end of this book, you’ll have gained the knowledge to write clean, efficient code that maximizes memory usage and boosts app performance.What you will learn Master memory allocation techniques to minimize resource wastage Differentiate between stack and heap memory, and use them efficiently Implement best practices for object lifetimes and garbage collection Understand .NET Core's memory management principles for optimal performance Identify and fix memory leaks to maintain application reliability Optimize memory usage in multithreaded and asynchronous applications Utilize memory profiling tools to pinpoint and resolve memory bottlenecks Apply advanced memory management techniques to enhance app scalability Who this book is for This book is for developers and professionals who are beyond the beginner stage and seek in-depth knowledge of memory management techniques within the context of .NET Core. Whether you are an experienced developer aiming to enhance application performance or an architect striving for optimal resource utilization, this book serves as a comprehensive guide to mastering memory management intricacies. To fully benefit from this book, you should have a solid understanding of C# programming and familiarity with the basics of .NET Core development. |
| gcp architecture diagram examples: Recent Trends in Computational Intelligence Enabled Research Siddhartha Bhattacharyya, Paramartha Dutta, Debabrata Samanta, Anirban Mukherjee, Indrajit Pan, 2021-07-31 The field of computational intelligence has grown tremendously over that past five years, thanks to evolving soft computing and artificial intelligent methodologies, tools and techniques for envisaging the essence of intelligence embedded in real life observations. Consequently, scientists have been able to explain and understand real life processes and practices which previously often remain unexplored by virtue of their underlying imprecision, uncertainties and redundancies, and the unavailability of appropriate methods for describing the incompleteness and vagueness of information represented. With the advent of the field of computational intelligence, researchers are now able to explore and unearth the intelligence, otherwise insurmountable, embedded in the systems under consideration. Computational Intelligence is now not limited to only specific computational fields, it has made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design to name a few. Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications explores the use of this computational paradigm across a wide range of applied domains which handle meaningful information. Chapters investigate a broad spectrum of the applications of computational intelligence across different platforms and disciplines, expanding our knowledge base of various research initiatives in this direction. This volume aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing. - Provides insights into the theory, algorithms, implementation, and application of computational intelligence techniques - Covers a wide range of applications of deep learning across various domains which are researching the applications of computational intelligence - Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques |
| gcp architecture diagram examples: Microsoft Defender for Cloud Yuri Diogenes, Tom Janetscheck, 2022-10-18 The definitive practical guide to Microsoft Defender for Cloud covering new components and multi-cloud enhancements! Microsoft Defender for Cloud offers comprehensive tools for hardening resources, tracking security posture, protecting against attacks, and streamlining security management – all in one natively integrated toolset. Now, leading Microsoft security experts Yuri Diogenes and Tom Janetscheck help you apply its robust protection, detection, and response capabilities throughout your operations, protecting workloads running on all your cloud, hybrid, and on-premises platforms. This guide shows how to make the most of new components, enhancements, and deployment scenarios, as you address today's latest threat vectors. Sharing best practices, expert tips, and optimizations only available from Microsoft's Defender for Cloud team, the authors walk through improving everything from policies and governance to incident response and risk management. Whatever your role or experience, they'll help you address new security challenges far more effectively—and save hours, days, or even weeks. Two of Microsoft's leading cloud security experts show how to: Assess new threat landscapes, the MITRE ATT&CK framework, and the implications of ''assume-breach'' Explore Defender for Cloud architecture, use cases, and adoption considerations including multicloud with AWS and GCP Plan for effective governance, successful onboarding, and maximum value Fully visualize complex cloud estates and systematically reduce their attack surfaces Prioritize risks with Secure Score, and leverage at-scale tools to build secure cloud-native apps Establish consistent policy enforcement to avoid drift Use advanced analytics and machine learning to identify attacks based on signals from all cloud workloads Enhance security posture by integrating with the Microsoft Sentinel SIEM/SOAR, Microsoft Purview, and Microsoft Defender for Endpoint Leverage just-in-time VM access and other enhanced security capabilities About This Book For architects, designers, implementers, SecOps professionals, developers, and security specialists working in Microsoft Azure environments For all IT professionals and decision-makers concerned with securing modern hybrid/multicloud environments, cloud-native apps, and PaaS services |
| gcp architecture diagram examples: Building News , 1876 |
| gcp architecture diagram examples: Data Analytics with Google Cloud Platform Murari Ramuka, 2019-12-16 Step-by-step guide to different data movement and processing techniques, using Google Cloud Platform Services DESCRIPTION Modern businesses are awash with data, making data-driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with enough knowledge of Cloud Computing in conjunction with Google Cloud Data platform to succeed in the role of a Cloud data expert. The current market is trending towards the latest cloud technologies, which is the need of the hour. Google being the pioneer, is dominating this space with the right set of cloud services being offered as part of GCP (Google Cloud Platform). At this juncture, this book will be very vital and will cover all the services that are being offered by GCP, putting emphasis on Data services. This book starts with sophisticated knowledge on Cloud Computing. It also explains different types of data services/technology and machine learning algorithm/Pre-Trained API through real-business problems, which are built on the Google Cloud Platform (GCP). With some of the latest business examples and hands-on guide, this book will enable the developers entering the data analytics fields to implement an end-to-end data pipeline, using GCP Data services. Through the course of the book, you will come across multiple industry-wise use cases, like Building Datawarehouse using Big Query, a sample real-time data analytics solution on machine learning and Artificial Intelligence that helped with the business decision, by employing a variety of data science approaches on Google Cloud environment. Whether yourÊbusinessÊis at the early stage of cloud implementation in its journey or well on its way to digital transformation,ÊGoogle Cloud'sÊsolutions and technologies will always help chart a path to success. This book can be used to develop the GCP concepts in an easy way. It contains many examples showcasing the implementation of a GCP service. It enables the learning of the basic and advance concepts of Google Cloud Data Platform. This book is divided into 7 chapters and provides a detailed description of the core concepts of each of the Data services offered by Google Cloud. KEY FEATURES Learn the basic concept of Cloud Computing along with different Cloud service provides with their supported Models (IaaS/PaaS/SaaS) Learn the basics of Compute Engine, App Engine, Container Engine, Project and Billing setup in the Google Cloud Platform Learn how and when to use Cloud DataFlow, Cloud DataProc and Cloud DataPrepÊ Build real-time data pipeline to support real-time analytics using Pub/Sub messaging service Setting up a fully managed GCP Big Data Cluster using Cloud DataProc for runningÊApache SparkÊandÊApache HadoopÊclusters in a simpler, more cost-efficient manner Learn how to use Cloud Data Studio for visualizing the data on top of Big Query Implement and understand real-world business scenarios for Machine Learning, Data Pipeline Engineering WHAT WILL YOU LEARN By the end of the book, you will have come across different data services and platforms offered by Google Cloud, and how those services/features can be enabled to serve business needs. You will also see a few case studies to put your knowledge to practice and solve business problems such as building a real-time streaming pipeline engine, Scalable Data Warehouse on Cloud, fully managed Hadoop cluster on Cloud and enabling TensorFlow/Machine Learning APIÕs to support real-life business problems. Remember to practice additional examples to master these techniques. WHO IS THIS BOOK FOR This book is for professionals as well as graduates who want to build a career in Google Cloud data analytics technologies. While no prior knowledge of Cloud Computing or related technologies is assumed, it will be helpful to have some data background and experience. One stop shop for those who wish to get an initial to advance understanding of the GCP data platform. The target audience will be data engineers/professionals who are new, as well as those who are acquainted with the tools and techniques related to cloud and data space.ÊÊ _Ê Ê Ê Individuals who have basic data understanding (i.e. Data and cloud) and have done some work in the field ofÊ data analytics, can refer/use this book to master their knowledge/understanding. _Ê Ê Ê The highlight of this book is that it will start with theÊ basic cloud computing fundamentals and will move on to cover the advance concepts on GCP cloud data analytics and hence can be referred across multiple different levels of audiences.Ê Table of Contents 1. GCP Overview and Architecture 2. Data Storage in GCPÊ 3. Data Processing in GCP with Pub/Sub and DataflowÊ 4. Data Processing in GCP with DataPrep and Dataflow 5. Big Query and Data Studio 6. Machine Learning with GCP 7. Sample Use cases and Examples |
| gcp architecture diagram examples: Multi-Cloud Architecture and Governance Jeroen Mulder, 2020-12-11 A comprehensive guide to architecting, managing, implementing, and controlling multi-cloud environments Key Features Deliver robust multi-cloud environments and improve your business productivity Stay in control of the cost, governance, development, security, and continuous improvement of your multi-cloud solution Integrate different solutions, principles, and practices into one multi-cloud foundation Book DescriptionMulti-cloud has emerged as one of the top cloud computing trends, with businesses wanting to reduce their reliance on only one vendor. But when organizations shift to multiple cloud services without a clear strategy, they may face certain difficulties, in terms of how to stay in control, how to keep all the different components secure, and how to execute the cross-cloud development of applications. This book combines best practices from different cloud adoption frameworks to help you find solutions to these problems. With step-by-step explanations of essential concepts and practical examples, you’ll begin by planning the foundation, creating the architecture, designing the governance model, and implementing tools, processes, and technologies to manage multi-cloud environments. You’ll then discover how to design workload environments using different cloud propositions, understand how to optimize the use of these cloud technologies, and automate and monitor the environments. As you advance, you’ll delve into multi-cloud governance, defining clear demarcation models and management processes. Finally, you’ll learn about managing identities in multi-cloud: who’s doing what, why, when, and where. By the end of this book, you’ll be able to create, implement, and manage multi-cloud architectures with confidenceWhat you will learn Get to grips with the core functions of multiple cloud platforms Deploy, automate, and secure different cloud solutions Design network strategy and get to grips with identity and access management for multi-cloud Design a landing zone spanning multiple cloud platforms Use automation, monitoring, and management tools for multi-cloud Understand multi-cloud management with the principles of BaseOps, FinOps, SecOps, and DevOps Define multi-cloud security policies and use cloud security tools Test, integrate, deploy, and release using multi-cloud CI/CD pipelines Who this book is for This book is for architects and lead engineers involved in architecting multi-cloud environments, with a focus on getting governance right to stay in control of developments in multi-cloud. Basic knowledge of different cloud platforms (Azure, AWS, GCP, VMWare, and OpenStack) and understanding of IT governance is necessary. |
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Google Cloud console
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Google Cloud Platform (GCP) - Sparx Systems
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Google Cloud Platform (GCP) - Sparx Systems
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Designing hybrid cloud architecture for the future
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Professional Cloud Architect
Section 1: Designing and planning a cloud solution architecture (~24% of the exam) 1.1 Designing a solution infrastructure that meets business requirements. Considerations include: Business …
Aws Eks Microservices Architecture Diagram - timehelper …
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Aws Network Diagram Examples - timehelper-beta.orases
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THE PROTOCOL DEVIATIONS HANDBOOK
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Guideline for the notification of serious breaches of …
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