“…5 architects : Eisenmann, Graves, Gwathmey, Hejduk, Meier…”

Tabla de Contenidos: “…Conté escrits sobre: Aldo Rossi, Robert Venturi, Five Architects, Pierre Chareau, New Brutalism, Carlo Scarpa i Mies van der Rohe, Escuela de Ticino, Minimalismo -- URV…”

Tabla de Contenidos: “…chapter 1 The Need for Change -- chapter 2 Transforming a Modern Telecom Network—From All-IP to Network Cloud -- chapter 3 Network Functions Virtualization -- chapter 4 Network Functions Virtualization Infrastructure -- chapter 5 Architecting the Network Cloud for High Availability -- chapter 6 Software-Defined Networking -- chapter 7 The Network Operating System: VNF Automation Platform -- chapter 8 Network Data and Optimization -- chapter 9 Network Security -- chapter 10 Enterprise Networks -- chapter 11 Network Access -- chapter 12 Network Edge -- chapter 13 Network Core -- chapter 14 Service Platforms -- chapter 15 Network Operations -- chapter 16 Network Measurements -- chapter 17 The Shift to Software -- chapter 18 What’s Next?…”

Tabla de Contenidos: “…CDI architecture and data hub components -- Ch. 5. Architecting for customer data integration -- Ch. 6. …”

Tabla de Contenidos: “…Building an effective data model -- Relational data warehouse schemas -- Evaluation of the schema design -- Designing the solution -- Implementing and unit testing the solution -- Summary -- Chapter 5: Architecting a Batch Processing Pipeline -- Technical requirements -- Developing the architecture and choosing the right tools -- Problem statement -- Analyzing the problem -- Architecting the solution -- Factors that affect your choice of storage -- Determining storage based on cost -- The cost factor in the processing layer -- Implementing the solution -- Profiling the source data…”

Tabla de Contenidos: “…Vertex AI Tables -- Interoperability with Vertex AI -- Access BigQuery Public Dataset -- Import BigQuery Data into Vertex AI -- Access BigQuery Data from Vertex AI Workbench Notebooks -- Analyze Test Prediction Data in BigQuery -- Export Vertex AI Batch Prediction Results -- Export BigQuery Models into Vertex AI -- BigQuery Design Patterns -- Hashed Feature -- Transforms -- Summary -- Exam Essentials -- Review Questions -- Appendix: Answers to Review Questions -- Chapter 1: Framing ML Problems -- Chapter 2: Exploring Data and Building Data Pipelines -- Chapter 3: Feature Engineering -- Chapter 4: Choosing the Right ML Infrastructure -- Chapter 5: Architecting ML Solutions -- Chapter 6: Building Secure ML Pipelines -- Chapter 7: Model Building -- Chapter 8: Model Training and Hyperparameter Tuning -- Chapter 9: Model Explainability on Vertex AI -- Chapter 10: Scaling Models in Production…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and credits -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Building Your App -- Chapter 1: Get Started with Kotlin Android Development -- Technical requirements -- Introduction to Kotlin -- Kotlin syntax, types, functions and classes -- Creating a Kotlin project -- Creating functions -- Creating classes -- Migrating from Java to Kotlin -- Kotlin features for Android developers -- Summary -- Chapter 2: Creating Your First Android App -- Technical requirements -- Android Studio overview -- Creating your Android app -- Exploring the new project -- Android Studio tips and tricks -- Some useful shortcuts -- Summary -- Chapter 3: Jetpack Compose Layout Basics -- Technical requirements -- Introduction to Jetpack Compose -- Declarative versus imperative UIs -- Composable functions -- Previews -- Modifiers -- Jetpack Compose layouts -- Column -- Row -- Box -- Lists -- Summary -- Chapter 4: Design with Material Design 3 -- Technical requirements -- Material Design 3 and its features -- Material components -- Using Material Design in our apps -- Adding Material Design 3 color schemes -- Designing UIs for large screens and foldables -- Using WindowSizeClass -- Making our app accessible -- Summary -- Part 2: Using Advanced Features -- Chapter 5: Architect Your App -- Technical Requirements -- Introduction to app architecture -- Deep Diving into MVVM -- How LazyColumn works -- Benefits of LazyColumn -- Creating a composable -- Jetpack libraries -- Dependency injection -- Migrating to Kotlin Gradle DSL and using version catalogs -- Migrating our app to Kotlin Gradle DSL -- Using a versions catalog -- Summary -- Chapter 6: Network Calls with Kotlin Coroutines -- Technical requirements -- Setting up Retrofit -- Introduction to Kotlin coroutines -- Coroutine basics…”

Tabla de Contenidos: “…List Orgs using the cf CLI -- Spaces -- Creating a Space using the Apps Manager -- Creating a Space using cf CLI -- List spaces using the cf CLI -- User accounts -- Create a user using the cf CLI -- Roles -- OrgManager role -- BillingManager role -- OrgAuditor role -- SpaceManager role -- SpaceDeveloper role -- SpaceAuditor role -- Assigning roles to a user using cf CLI -- Summary -- Chapter 5: Architecting and Building Apps for the Cloud -- What is a cloud-native application? …”

Tabla de Contenidos: “…-- Leveraging "Architect Products, Not Just Solutions for Projects" in Practice -- Principle 2: Focus on Quality Attributes, Not on Functional Requirements -- Principle 3: Delay Design Decisions Until They Are Absolutely Necessary -- Principle 4: Architect for Change-Leverage "The Power of Small" -- Principle 5: Architect for Build, Test, and Deploy -- Principle 6: Model the Organization After the Design of the System -- Summary -- Endnotes -- 3 Getting Started with Continuous Architecture: Requirements Management -- Functional Requirements versus Quality Attributes -- Leveraging Value Chains to Understand the Functional Scope of a Product -- The Big Picture: The Value Chain -- The Dynamics: User Story Themes -- Analyzing User Story Themes -- Using Architecture Scenarios for Quality Attribute Requirements -- Building the Quality Attributes Utility Tree -- Quality Attribute Details -- Performance -- Availability -- Cost effectiveness -- Gathering and Managing Requirements -- Stakeholder Interviews -- Joint Requirements Development Sessions -- Conversations with Business Partners (or with the Product Owner)…”

Tabla de Contenidos: “…Cataloging your data to avoid the data swamp -- How to avoid the data swamp -- The AWS Glue/Lake Formation data catalog -- AWS services for data encryption and security monitoring -- AWS Key Management Service (KMS) -- Amazon Macie -- Amazon GuardDuty -- AWS services for managing identity and permissions -- AWS Identity and Access Management (IAM) service -- Using AWS Lake Formation to manage data lake access -- Hands-on - configuring Lake Formation permissions -- Creating a new user with IAM permissions -- Transitioning to managing fine-grained permissions with AWS Lake Formation -- Summary -- Section 2: Architecting and Implementing Data Lakes and Data Lake Houses -- Chapter 5: Architecting Data Engineering Pipelines -- Technical requirements -- Approaching the data pipeline architecture -- Architecting houses and architecting pipelines -- Whiteboarding as an information-gathering tool -- Conducting a whiteboarding session -- Identifying data consumers and understanding their requirements -- Identifying data sources and ingesting data -- Identifying data transformations and optimizations -- File format optimizations -- Data standardization -- Data quality checks -- Data partitioning -- Data denormalization -- Data cataloging -- Whiteboarding data transformation -- Loading data into data marts -- Wrapping up the whiteboarding session -- Hands-on - architecting a sample pipeline -- Detailed notes from the project "Bright Light" whiteboarding meeting of GP Widgets, Inc -- Summary -- Chapter 6: Ingesting Batch and Streaming Data -- Technical requirements -- Understanding data sources -- Data variety -- Data volume -- Data velocity -- Data veracity -- Data value -- Questions to ask -- Ingesting data from a relational database -- AWS Database Migration Service (DMS) -- AWS Glue -- Other ways to ingest data from a database…”

Tabla de Contenidos: “…Chapter 4: Data Governance, Security, and Cataloging -- Technical requirements -- The many different aspects of data governance -- Data security, access, and privacy -- Common data regulatory requirements -- Core data protection concepts -- Personally identifiable information (PII) -- Personal data -- Encryption -- Anonymized data -- Pseudonymized data/tokenization -- Authentication -- Authorization -- Putting these concepts together -- Data quality, data profiling, and data lineage -- Data quality -- Data profiling -- Data lineage -- Business and technical data catalogs -- Implementing a data catalog to avoid creating a data swamp -- Business data catalogs -- Technical data catalogs -- AWS services that help with data governance -- The AWS Glue/Lake Formation technical data catalog -- AWS Glue DataBrew for profiling datasets -- AWS Glue Data Quality -- AWS Key Management Service (KMS) for data encryption -- Amazon Macie for detecting PII data in Amazon S3 objects -- The AWS Glue Studio Detect PII transform for detecting PII data in datasets -- Amazon GuardDuty for detecting threats in an AWS account -- AWS Identity and Access Management (IAM) service -- Using AWS Lake Formation to manage data lake access -- Permissions management before Lake Formation -- Permissions management using AWS Lake Formation -- Hands-on - configuring Lake Formation permissions -- Creating a new user with IAM permissions -- Transitioning to managing fine-grained permissions with AWS Lake Formation -- Activating Lake Formation permissions for a database and table -- Granting Lake Formation permissions -- Summary -- Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations -- Chapter 5: Architecting Data Engineering Pipelines -- Technical requirements -- Approaching the data pipeline architecture -- Architecting houses and pipelines…”

“… This book will help you understand the capabilities of Microsoft Hyper-V, architect a Hyper-V solution for your datacenter, plan a deployment/migration, and then manage it all using native tools and System Center. …”

Tabla de Contenidos: “…-- 1 Introducing modern Java -- 1.1 The language and the platform -- 1.2 The new Java release model -- 1.3 Enhanced type inference (var keyword) -- 1.4 Changing the language and the platform -- 1.4.1 Sprinkling some sugar -- 1.4.2 Changing the language -- 1.4.3 JSRs and JEPs -- 1.4.4 Incubating and preview features -- 1.5 Small changes in Java 11 -- 1.5.1 Collections factories (JEP 213) -- 1.5.2 Remove enterprise modules (JEP 320) -- 1.5.3 HTTP/2 (Java 11) -- 1.5.4 Single-file source-code programs (JEP 330) -- Summary -- 2 Java modules -- 2.1 Setting the scene -- 2.1.1 Project Jigsaw -- 2.1.2 The module graph -- 2.1.3 Protecting the internals -- 2.1.4 New access control semantics -- 2.2 Basic modules syntax -- 2.2.1 Exporting and requiring -- 2.2.2 Transitivity -- 2.3 Loading modules -- 2.3.1 Platform modules -- 2.3.2 Application modules -- 2.3.3 Automatic modules -- 2.3.4 Unnamed module -- 2.4 Building a first modular app -- 2.4.1 Command-line switches for modules -- 2.4.2 Executing a modular app -- 2.4.3 Modules and reflection -- 2.5 Architecting for modules -- 2.5.1 Split packages -- 2.5.2 Java 8 Compact Profiles -- 2.5.3 Multi-release JARs -- 2.6 Beyond modules -- Summary -- 3 Java 17 -- 3.1 Text Blocks -- 3.2 Switch Expressions -- 3.3 Records -- 3.3.1 Nominal typing -- 3.3.2 Compact record constructors -- 3.4 Sealed Types -- 3.5 New form of instanceof -- 3.6 Pattern Matching and preview features…”

“…Discover the capabilities of Microsoft Hyper-V Architect a Hyper-V datacenter solution Plan and manage a deployment or migration Integrate complementary technologies for full scalability Data is everywhere—on desktops, laptops, phones, and multiple operating systems, accessed through email, text messages, web searches, online services, and more. …”