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As a working software developer, you know how to complete your tasks with solid code, whether it's on the frontend or backend. Now you're ready to move to the next level in your career, and you need to understand the subtle yet deep skills it takes to become a senior developer. This practical book shows you everything it takes to create a full-stack web application hosted on a cloud platform.Senior staff engineer Milecia McGregor helps you see how the whole system works and how senior developers arrive at technical decisions. You'll learn about design and development principles and when to apply them. You'll also discover strategies for working with different teams and understand how the product team makes its decisions.In four parts, this book shows you how to:Translate designs into tasks and learn the questions you'll need to ask the product teamWalk through development considerations for the backend like overall architecture, security, and third-party servicesBuild the project's user interface as well as state and data management, performance, and other concerns associated with frontend appsConnect the frontend, backend, and other systems and deploy a full stack app to production

To ensure that applications are reliable and always available, more businesses today are moving applications to AWS. But many companies still struggle to design and build these cloud applications effectively, thinking that because the cloud is resilient, their applications will be too. With this practical guide, software, DevOps, and cloud engineers will learn how to implement resilient designs and configurations in the cloud using hands-on independent labs.Authors Kevin Schwarz, Jennifer Moran, and Dr. Nate Bachmeier from AWS teach you how to build cloud applications that demonstrate resilience with patterns like back off and retry, multi-Region failover, data protection, and circuit breaker with common configuration, tooling, and deployment scenarios. Labs are organized into categories based on complexity and topic, making it easy for you to focus on the most relevant parts of your business.You'll learn how to:Configure and deploy AWS services using resilience patternsImplement stateless microservices for high availabilityConsider multi-Region designs to meet business requirementsImplement backup and restore, pilot light, warm standby, and active-active strategiesBuild applications that withstand AWS Region and Availability Zone impairmentsUse chaos engineering experiments for fault injection to test for resilienceAssess the trade-offs when building resilient systems, including cost, complexity, and operational burden

Performance tuning is an experimental science, but that doesn't mean engineers should resort to guesswork and folklore to get the job done. Yet that's often the case. With this practical book, intermediate to advanced Java technologists working with complex platforms will learn how to tune Java cloud applications for performance using a quantitative, verifiable, and repeatable approach.In response to the ubiquity of cloud computing, this updated edition of Optimizing Cloud Native Java addresses topics that are key to high performance of Java applications in the cloud. Many resources on performance tend to focus on the theory and internals of Java virtual machines, but this book discusses the low-level technical aspects within the context of performance-tuning practicalities and examines a wide range of aspects.With this book, you will:Learn how Java principles and technology make the best use of modern hardware, operating systems, and cloud stacksExamine the pitfalls of measuring Java performance numbers and the drawbacks of microbenchmarkingUnderstand how to package, deploy, operate, and debug Java/JVM applications in modern cloud environmentsApply emerging observability approaches to obtain deep understanding of cloud native applicationsUse Java language performance techniques including concurrent and distributed forms

ActivityPub is the new standard for connecting social networks together on the social web. This open, decentralized social networking protocol defines an API for sharing activities to a social network and a procedure that servers use to distribute those activities to a subscriber's feed. With this book, you'll learn how to assemble ActivityPub-enabled clients for making new kinds of social apps on top of existing networks and build ActivityPub servers that create new human or automated accounts on the social web.With those skills under your belt, you can explore other applications of this publish-subscribe technology: content management systems, internet of things, and enterprise automation. With hands-on examples and in-depth knowledge from Evan Prodromou, one of the authors of the protocol, this is the ActivityPub handbook that every social software hacker needs.Learn what the social web is and what ActivityPub doesRepresent social network data in the Activity Streams 2.0 formatWrite a simple ActivityPub client and serverExtend ActivityPub's functionality with new typesFind in-depth details about the more obscure ActivityPub data structuresEvan Prodromou is a coauthor of the ActivityPub protocol and the Activity Streams 2.0 data format.

Real-time applications are becoming the norm today. But building a model that works properly requires real-time data from the source, in-flight stream processing, and low latency serving of its analytics. With this practical book, data engineers, data architects, and data analysts will learn how to use streaming databases to build real-time solutions.Authors Hubert Dulay and Ralph M. Debusmann take you through streaming database fundamentals, including how these databases reduce infrastructure for real-time solutions. You'll learn the difference between streaming databases, stream processing, and real-time online analytical processing (OLAP) databases. And you'll discover when to use push queries versus pull queries, and how to serve synchronous and asynchronous data emanating from streaming databases.This guide helps you:Explore stream processing and streaming databasesLearn how to build a real-time solution with a streaming databaseUnderstand how to construct materialized views from any number of streamsLearn how to serve synchronous and asynchronous dataGet started building low-complexity streaming solutions with minimal setup

Get the details, examples, and best practices you need to build generative AI applications, services, and solutions using the power of Azure OpenAI Service. With this comprehensive guide, Microsoft AI specialist Adrián González Sánchez examines the integration and utilization of Azure OpenAI Service—using powerful generative AI models such as GPT-4 and GPT-4o—within the Microsoft Azure cloud computing platform.To guide you through the technical details of using Azure OpenAI Service, this book shows you how to set up the necessary Azure resources, prepare end-to-end architectures, work with APIs, manage costs and usage, handle data privacy and security, and optimize performance. You'll learn various use cases where Azure OpenAI Service models can be applied, and get valuable insights from some of the most relevant AI and cloud experts.Ideal for software and cloud developers, product managers, architects, and engineers, as well as cloud-enabled data scientists, this book will help you:Learn how to implement cloud native applications with Azure OpenAI ServiceDeploy, customize, and integrate Azure OpenAI Service with your applicationsCustomize large language models and orchestrate knowledge with company-owned dataUse advanced roadmaps to plan your generative AI projectEstimate cost and plan generative AI implementations for adopter companies

How will software development and operations have to change to meet the sustainability and green needs of the planet? And what does that imply for development organizations? In this eye-opening book, sustainable software advocates Anne Currie, Sarah Hsu, and Sara Bergman provide a unique overview of this topic—discussing everything from the likely evolution of national grids to the effect those changes will have on the day-to-day lives of developers.Ideal for everyone from new developers to CTOs, Building Green Software tackles the challenges involved and shows you how to build, host, and operate code in a way that's not only better for the planet but also cheaper and relatively low-risk for your business. Most hyperscale public cloud providers have already committed to net-zero IT operations by 2030. This book shows you how to get on board.You'll explore:How the energy transition is likely to change hosting on prem and in the cloud—and how your company can prepareThe fundamental architectural principles of sustainable software development and how to apply themHow to determine which parts of your system need to changeThe concept of extending hardware longevity and the part that software plays

With rapidly changing architecture and API-driven automation, cloud platforms come with unique security challenges and opportunities. In this updated second edition, you'll examine security best practices for multivendor cloud environments, whether your company plans to move legacy on-premises projects to the cloud or build a new infrastructure from the ground up. Developers, IT architects, and security professionals will learn cloud-specific techniques for securing popular cloud platforms such as Amazon Web Services, Microsoft Azure, and IBM Cloud. IBM Distinguished Engineer Chris Dotson shows you how to establish data asset management, identity and access management (IAM), vulnerability management, network security, and incident response in your cloud environment. Learn the latest threats and challenges in the cloud security spaceManage cloud providers that store or process data or deliver administrative controlLearn how standard principles and concepts—such as least privilege and defense in depth—apply in the cloudUnderstand the critical role played by IAM in the cloudUse best tactics for detecting, responding, and recovering from the most common security incidentsManage various types of vulnerabilities, especially those common in multicloud or hybrid cloud architecturesExamine privileged access management in cloud environments

Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare.Authors Kerrie Holley, former Google healthcare professionals, guide you through the transformative potential of large language models (LLMs) and generative AI in healthcare. From personalized patient care and clinical decision support to drug discovery and public health applications, this comprehensive exploration covers real-world uses and future possibilities of LLMs and generative AI in healthcare.With this book, you will:Understand the promise and challenges of LLMs in healthcareLearn the inner workings of LLMs and generative AIExplore automation of healthcare use cases for improved operations and patient care using LLMsDive into patient experiences and clinical decision-making using generative AIReview future applications in pharmaceutical R&D, public health, and genomicsUnderstand ethical considerations and responsible development of LLMs in healthcare'The authors illustrate generative's impact on drug development, presenting real-world examples of its ability to accelerate processes and improve outcomes across the pharmaceutical industry.'--Harsh Pandey, VP, Data Analytics & Business Insights, Medidata-DassaultKerrie Holley is a retired Google tech executive, IBM Fellow, and VP/CTO at Cisco. Holley's extensive experience includes serving as the first Technology Fellow at United Health Group (UHG), Optum, where he focused on advancing and applying AI, deep learning, and natural language processing in healthcare.Manish Mathur brings over two decades of expertise at the crossroads of healthcare and technology. A former executive at Google and Johnson & Johnson, he now serves as an independent consultant and advisor. He guides payers, providers, and life sciences companies in crafting cutting-edge healthcare solutions.

Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud platform. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data.Author Joyce Kay Avila demonstrates how to use Salesforce's native connectors, canonical data model, and Einstein's built-in trust layer to accelerate your time to value. You'll learn how to leverage Salesforce's low-code/no-code functionality to expertly build a Data Cloud foundation that unlocks the power of structured and unstructured data. Use Data Cloud tools to build your own predictive models or leverage third-party machine learning platforms like Amazon SageMaker, Google Vertex AI, and Databricks.This book will help you:Develop a plan to execute a CDP project effectively and efficientlyConnect Data Cloud to external data sources and build out a Customer 360 Data ModelLeverage data sharing capabilities with Snowflake, BigQuery, Databricks, and AzureUse Salesforce Data Cloud capabilities for identity resolution and segmentationCreate calculated, streaming, visualization, and predictive insightsUse Data Graphs to power Salesforce Einstein capabilitiesLearn Data Cloud best practices for all phases of the development lifecycle

In the modern symphony of business, each section-from the technical to the managerial-must play in harmony. Authors Ron Itelman and Juan Cruz Viotti introduce a bold methodology to synchronize your business and technical teams, transforming them into a single, high-performing unit. Misalignment between business and technical teams halts innovation. You'll learn how to transcend the root causes of project failure-the ambiguity, knowledge gaps, and blind spots that lead to wasted efforts. The unifying methodology in this book will teach you these alignment tools and more: The four facets of data products: A simple blueprint that encapsulates data and business logic helps eliminate the most common causes of wasted time and misunderstandingThe concept compass: An easy way to identify the biggest sources of misalignmentSuccess spectrums: Define the required knowledge and road map your team needs to achieve successJSON Schema: Leverage JSON and JSON Schema to technically implement the strategy at scale, including extending JSON Schema with custom keywords, understanding JSON Schema annotations, and hosting your own schema registryData hygiene: Learn how to design high-quality datasets aligned with creating real business value, and protect your organization from the most common sources of pain

Learn how to use Go's strengths to develop services that are scalable and resilient even in an unpredictable environment. With this book's expanded second edition, Go developers will explore the composition and construction of cloud native applications, from lower-level Go features and mid-level patterns to high-level architectural considerations.Each chapter in this new edition builds on the lessons of the previous chapter, taking intermediate to advanced developers through Go to construct a simple but fully featured distributed key-value store. You'll learn about Go generics, dependability and reliability, memory leaks, and message-oriented middleware. New chapters on security and distributed state delve into critical aspects of developing secure distributed cloud native applications.With this book you will:Learn the features that make Go an ideal language for building cloud native softwareUnderstand how Go solves the challenges of designing scalable distributed servicesDesign and implement a reliable cloud native service by leveraging Go's lower-level features such as channels and goroutinesApply patterns, abstractions, and tooling to effectively build and manage complex distributed systemsOvercome stumbling blocks when using Go to build and manage a cloud native service

Learn how to prepare for—and pass—the Kubernetes and Cloud Native Associate (KCNA) certification exam. This practical guide serves as both a study guide and point of entry for practitioners looking to explore and adopt cloud native technologies. Adrián González Sánchez and Jorge Valenzuela Jiménez teach you not only the core technology fundamentals, but also the community and industry that KCNA serves.With the meteoric rise in cloud adoption, cloud native technologies such as Kubernetes have become the de facto industry standard. Other Kubernetes certifications—including KCSA, CKAD, CKA, and CKS—are all geared toward higher-level technical proficiency. The KCNA is the entry door to your cloud native journey, and the certification exam covers the cloud native environment generally as well as fundamental Kubernetes skills and knowledge.This guide helps you learn:How to best and most efficiently prepare for the KCNA examThe latest cloud native developments and their importanceThe fundamentals of Kubernetes, cloud native development, and related CNCF projectsThe core elements of Kubernetes applicationsThe crucial elements of modern cloud native developmentHow to differentiate and choose cloud native technologiesThe market value of passing the KCNA examInsights and testimonials from key cloud native industry experts

Microservices can be a very effective approach for delivering value to your organization and to your customers. If you get them right, microservices help you to move fast by making changes to small parts of your system hundreds of times a day. But if you get them wrong, microservices will just make everything more complicated.In this book, technical engineering leader Sarah Wells provides practical, in-depth advice for moving to microservices. Having built her first microservice architecture in 2013 for the Financial Times, Sarah discusses the approaches you need to take from the start and explains the potential problems most likely to trip you up. You'll also learn how to maintain the architecture as your systems mature while minimizing the time you spend on support and maintenance. With this book, you will:Learn the impact of microservices on software development patterns and practicesIdentify the organizational changes you need to make to successfully build and operate this architectureDetermine the steps you must take before you move to microservicesUnderstand the traps to avoid when you create a microservices architecture—and learn how to recover if you fall into one

Cloud native development gives you the power to rapidly build, secure, and scale software. But you still need to navigate many potential pitfalls along the way. Through practical examples, this book demonstrates how to use Google Cloud as a laboratory to enable rapid innovation, a factory to automate build and testing, and a citadel to operate applications at scale securely.Author Daniel Vaughan shows you how to take applications from prototype to production by combining Google Cloud services, a cloud native programming model, and best practices. By following an example project from start to finish, developers, architects, and engineering managers working with the Google Cloud Platform will learn how to build and run cloud native applications on Google Cloud with confidence.With this book, you will:Understand cloud native development concepts including microservices, containerization, and event-driven architectureLearn Google Cloud services that specifically support this development style: compute, persistence, messaging, DevOps, security and networking, and observabilityConfidently build cloud native applications on Google CloudLearn how to address nonfunctional requirements such as security, observability, and testingSuccessfully make the transition from initial proofs of concept and prototypes to production systems

Every distributed system strives for reliability, performance, and quality, but building such a system is hard. Establishing a set of design patterns enables software developers and system architects to use a common language to describe their systems and learn from the patterns and practices developed by others.The popularity of containers and Kubernetes paves the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help guide the systems you build using common patterns and practices drawn from some of the highest performing distributed systems in use today. These common patterns make the systems you build far more approachable and efficient, even if you've never built a distributed system before.Author Brendan Burns demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system.This fully updated second edition includes new chapters on AI inference, AI training, and building robust systems for the real world.Understand how patterns and reusable components enable the rapid development of reliable distributed systemsUse the sidecar, adapter, and ambassador patterns to split your application into a group of containers on a single machineExplore loosely coupled multinode distributed patterns for replication, scaling, and communication between componentsLearn distributed system patterns for large-scale batch data processing covering work queues, event-based processing, and coordinated workflows

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 communicationRoles that security architects perform and how the techniques relate to nonsecurity subject matter expertsHow security solution architecture is related to design thinking, enterprise security architecture, and engineeringHow architects can integrate security into a solution architecture for applications and infrastructure using a consistent end-to-end set of practicesHow to apply architectural thinking to the development of new security solutionsAbout the authorsMark 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.

All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks.Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage.You'll learn how to:Design a modern and secure cloud native or hybrid data analytics and machine learning platformAccelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platformDemocratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilitiesEnable your business to make decisions in real time using streaming pipelinesBuild an MLOps platform to move to a predictive and prescriptive analytics approach

In today's fast-paced world, more and more organizations require rapid application development with reduced development costs and increased productivity. This practical guide shows application developers how to use PowerApps, Microsoft's no-code/low-code application framework that helps developers speed up development, modernize business processes, and solve tough challenges.Author Arpit Shrivastava provides a comprehensive overview of designing and building cost-effective applications with Microsoft Power Apps. You'll learn fundamental concepts behind low-code and no-code development, how to build applications using pre-built and blank templates, how to design an app using Copilot AI and drag and drop PowerPoint-like controls, use Excel-like expressions to write business logic for an app, and integrate apps with external data sources.With this book, you'll:Learn the importance of no-code/low-code application developmentDesign mobile/tablet (canvas apps) applications using pre-built and blank templatesDesign web applications (model-driven apps) using low-code, no-code, and pro-code componentsIntegrate PowerApps with external applicationsLearn basic coding concepts like JavaScript, Power Fx, and C#Apply best practices to customize Dynamics 365 CE applicationsDive into Azure DevOps and ALM concepts to automate application deployment

Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help.Authors Ethan Cowan, Michael Shoemate, and Mayana Pereira explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and understand how to use open source tools to create differentially private statistics, explore how to assess the utility/privacy trade-offs, and learn how to integrate differential privacy into workflows.With this book, you'll learn:How DP guarantees privacy when other data anonymization methods don'tWhat preserving individual privacy in a dataset entailsHow to apply DP in several real-world scenarios and datasetsPotential privacy attack methods, including what it means to perform a reidentification attackHow to use the OpenDP library in privacy-preserving data releasesHow to interpret guarantees provided by specific DP data releases

Amazon Redshift powers analytic cloud data warehouses worldwide, from startups to some of the largest enterprise data warehouses available today. This practical guide thoroughly examines this managed service and demonstrates how you can use it to extract value from your data immediately, rather than go through the heavy lifting required to run a typical data warehouse.Analytic specialists Rajesh Francis, Rajiv Gupta, and Milind Oke detail Amazon Redshift's underlying mechanisms and options to help you explore out-of-the box automation. Whether you're a data engineer who wants to learn the art of the possible or a DBA looking to take advantage of machine learning-based auto-tuning, this book helps you get the most value from Amazon Redshift.By understanding Amazon Redshift features, you'll achieve excellent analytic performance at the best price, with the least effort. This book helps you:Build a cloud data strategy around Amazon Redshift as foundational data warehouseGet started with Amazon Redshift with simple-to-use data models and design best practicesUnderstand how and when to use Redshift Serverless and Redshift provisioned clustersTake advantage of auto-tuning options inherent in Amazon Redshift and understand manual tuning optionsTransform your data platform for predictive analytics using Redshift ML and break silos using data sharingLearn best practices for security, monitoring, resilience, and disaster recoveryLeverage Amazon Redshift integration with other AWS services to unlock additional value

Explore the theory and practice of designing and writing serverless applications using examples from the Knative project. With this practical guide, mid-level to senior application developers and team managers will learn when and why to target serverless platforms when developing microservices or applications. Along the way, you'll also discover warning signs that suggest cases when serverless might cause you more trouble than joy.Drawing on author Evan Anderson's 15 years of experience developing and maintaining applications in the cloud, and more than 6 years of experience with serverless platforms at scale, this book acts as your guide into the high-velocity world of serverless application development. You'll come to appreciate why Knative is the most widely adopted open source serverless platform available. With this book, you will:Learn what serverless is, how it works, and why teams are adopting itUnderstand the benefits of Knative for cloud native development teamsLearn how to build a serverless application on KnativeExplore the challenges serverless introduces for debugging and the tools that can help improve itLearn why event-driven architecture and serverless compute are complementary but distinctUnderstand when a serverless approach might not be the right system design

Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images.Apply generative AI to your business use casesDetermine which generative AI models are best suited to your task Perform prompt engineering and in-context learningFine-tune generative AI models on your datasets with low-rank adaptation (LoRA)Align generative AI models to human values with reinforcement learning from human feedback (RLHF)Augment your model with retrieval-augmented generation (RAG)Explore libraries such as LangChain and ReAct to develop agents and actionsBuild generative AI applications with Amazon Bedrock

The software development ecosystem is constantly changing, providing a constant stream of new tools, frameworks, techniques, and paradigms. Over the past few years, incremental developments in core engineering practices for software development have created the foundations for rethinking how architecture changes over time, along with ways to protect important architectural characteristics as it evolves. This practical guide ties those parts together with a new way to think about architecture and time.

Many organizations today have begun to modernize their Windows workloads to take full advantage of cloud economics. If you're a C# developer at one of these companies, you need options for rehosting, replatforming, and refactoring your existing.NET Framework applications. This practical book guides you through the process of converting your monolithic application to microservices on AWS.Authors Noah Gift, founder of Pragmatic AI Labs, and James Charlesworth, engineering manager at Pendo, take you through the depth and breadth of.NET tools on AWS. You'll examine modernization techniques and pathways for incorporating Linux and Windows containers and serverless architecture to build, maintain, and scale modern.NET apps on AWS. With this book, you'll learn how to make your applications more modern, resilient, and cost-effective.Get started building solutions with C# on AWSLearn DevOps best practices for AWSExplore the development tools and services that AWS providesSuccessfully migrate a legacy.NET application to AWSDevelop serverless.NET microservices on AWSContainerize your.NET applications and move into the cloudMonitor and test your AWS.NET applicationsBuild cloud native solutions that combine the best of the.NET platform and AWS

As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization.Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed.Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabricGo deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and moreExplore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata

The cloud promises cost savings, agility, and more. But the increasing complexity of modern IT systems often prevents businesses from realizing the outcomes they sought by moving to the cloud in the first place. At the core of this complexity is technical debt. Ad hoc decisions, traditional approaches, and'quick and dirty'solutions add up to fragile, more complex systems (and organizations)--reducing the pace of change, driving up costs, and degrading security. To succeed in the cloud, you first have to manage the problems IT complexity creates. This practical guide will help.If you're tasked with designing, delivering, or approving IT systems that support business growth, this book will teach you the principles and strategies you need to break out of silos, reassess traditional thinking, provide the business a better competitive position, and achieve cloud success across the company.You'll learn:How to assess your current IT environment to identify complexity-centered risksHow to structure cross-functional teams to reduce friction pointsHow to create a framework for IT investments that's aligned with your business goalsHow to measure your outcomes for continued success

This practical guide bridges the gap between general cloud computing architecture in Microsoft Azure and scientific computing for bioinformatics and genomics. You'll get a solid understanding of the architecture patterns and services that are offered in Azure and how they might be used in your bioinformatics practice. You'll get code examples that you can reuse for your specific needs. And you'll get plenty of concrete examples to illustrate how a given service is used in a bioinformatics context.You'll also get valuable advice on how to:Use enterprise platform services to easily scale your bioinformatics workloadsOrganize, query, and analyze genomic data at scaleBuild a genomics data lake and accompanying data warehouseUse Azure Machine Learning to scale your model training, track model performance, and deploy winning modelsOrchestrate and automate processing pipelines using Azure Data Factory and DatabricksCloudify your organization's existing bioinformatics pipelines by moving your workflows to Azure high-performance compute servicesAnd more

If your organization plans to modernize services and move to the cloud from legacy software or a private cloud on premises, this book is for you. Software developers, solution architects, cloud engineers, and anybody interested in cloud technologies will learn fundamental concepts for cloud computing, migration, transformation, and development using Microsoft Azure.Author and Microsoft MVP Jonah Carrio Andersson guides you through cloud computing concepts and deployment models, the wide range of modern cloud technologies, application development with Azure, team collaboration services, security services, and cloud migration options in Microsoft Azure.You'll gain insight into the Microsoft Azure cloud services that you can apply in different business use cases, software development projects, and modern solutions in the cloud. You'll also become fluent with Azure cloud migration services, serverless computing technologies that help your development team work productively, Azure IoT, and Azure cognitive services that make your application smarter. This book also provides real-world advice and best practices based on the author's own Azure migration experience.Gain insight into which Azure cloud service best suits your company's particular needsUnderstand how to use Azure for different use cases and specific technical requirementsStart developing cloud services, applications, and solutions in the Azure environmentLearn how to migrate existing legacy applications to Microsoft Azure

Cybersecurity is broken. Year after year, attackers remain unchallenged and undeterred, while engineering teams feel pressure to design, build, and operate'secure'systems. Failure can't be prevented, mental models of systems are incomplete, and our digital world constantly evolves. How can we verify that our systems behave the way we expect? What can we do to improve our systems'resilience? In this comprehensive guide, authors Kelly Shortridge and Aaron Rinehart help you navigate the challenges of sustaining resilience in complex software systems by using the principles and practices of security chaos engineering. By preparing for adverse events, you can ensure they don't disrupt your ability to innovate, move quickly, and achieve your engineering and business goals. Learn how to design a modern security programMake informed decisions at each phase of software delivery to nurture resilience and adaptive capacityUnderstand the complex systems dynamics upon which resilience outcomes dependNavigate technical and organizational trade-offsthat distort decision making in systemsExplore chaos experimentation to verify critical assumptions about software quality and securityLearn how major enterprises leverage security chaos engineering

The way developers design, build, and run software has changed significantly with the evolution of microservices and containers. These modern architectures offer new distributed primitives that require a different set of practices than many developers, tech leads, and architects are accustomed to. With this focused guide, Bilgin Ibryam and Roland Huss provide common reusable patterns and principles for designing and implementing cloud native applications on Kubernetes.Each pattern includes a description of the problem and a Kubernetes-specific solution. All patterns are backed by and demonstrated with concrete code examples. This updated edition is ideal for developers and architects familiar with basic Kubernetes concepts who want to learn how to solve common cloud native challenges with proven design patterns.You'll explore:Foundational patterns covering core principles and practices for building and running container-based cloud native applicationsBehavioral patterns that delve into finer-grained concepts for managing various types of container and platform interactionsStructural patterns for organizing containers within a Pod for addressing specific use casesConfiguration patterns that provide insight into how application configurations can be handled in KubernetesSecurity patterns for hardening the access to cloud native applications running on KubernetesAdvanced patterns covering more complex topics such as operators and autoscaling

FinOps brings financial accountability to the variable spend model of cloud. Used by the majority of global enterprises, this management practice has grown from a fringe activity to the de facto discipline managing cloud spend. In this book, authors J.R. Storment and Mike Fuller outline the process of building a culture of cloud FinOps by drawing on real-world successes and failures of large-scale cloud spenders.Engineering and finance teams, executives, and FinOps practitioners alike will learn how to build an efficient and effective FinOps machine for data-driven cloud value decision-making. Complete with a road map to get you started, this revised second edition includes new chapters that cover forecasting, sustainability, and connectivity to other frameworks.You'll learn:The DNA of a highly functional cloud FinOps cultureA road map to build executive support for FinOps adoptionHow to understand and forecast your cloud spendingHow to empower engineering and finance to work togetherCost allocation strategies to create accountability for cloud and container spendStrategies for rate discounts from cloud commitmentsWhen and how to implement automation of repetitive cost tasksHow to empower engineering team action on cost efficiency

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems.Practical Data Privacy answers important questions such as:What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases?What does'anonymized data'really mean? How do I actually anonymize data?How does federated learning and analysis work?Homomorphic encryption sounds great, but is it ready for use?How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help?How do I ensure that my data science projects are secure by default and private by design?How do I work with governance and infosec teams to implement internal policies appropriately?

Is Kubernetes ready for stateful workloads? This open source system has become the primary platform for deploying and managing cloud native applications. But because it was originally designed for stateless workloads, working with data on Kubernetes has been challenging. If you want to avoid the inefficiencies and duplicative costs of having separate infrastructure for applications and data, this practical guide can help.Using Kubernetes as your platform, you'll learn open source technologies that are designed and built for the cloud. Authors Jeff Carpenter and Patrick McFadin provide case studies to help you explore new use cases and avoid the pitfalls others have faced. You'll get an insider's view of what's coming from innovators who are creating next-generation architectures and infrastructure.With this book, you will:Learn how to use basic Kubernetes resources to compose data infrastructureAutomate the deployment and operations of data infrastructure on Kubernetes using tools like Helm and operatorsEvaluate and select data infrastructure technologies for use in your applicationsIntegrate data infrastructure technologies into your overall stackExplore emerging technologies that will enhance your Kubernetes-based applications in the future

In this practical guide, four Kubernetes professionals with deep experience in distributed systems, enterprise application development, and open source will guide you through the process of building applications with this container orchestration system. They distill decades of experience from companies that are successfully running Kubernetes in production and provide concrete code examples to back the methods presented in this book.Revised to cover all the latest Kubernetes features, new tooling, and deprecations, this book is ideal for those who are familiar with basic Kubernetes concepts but want to get up to speed on the latest best practices. You'll learn exactly what you need to know to build your best app with Kubernetes the first time.Set up and develop applications in KubernetesLearn patterns for monitoring, securing your systems, and managing upgrades, rollouts, and rollbacksIntegrate services and legacy applications and develop higher-level platforms on top of KubernetesRun machine learning workloads in KubernetesEnsure pod and container securityUnderstand issues that have become increasingly critical to the successful implementation of Kubernetes, such as chaos engineering/testing, GitOps, service mesh, and observability

Kubernetes has gained significant popularity over the past few years, with OpenShift as one of its most mature and prominent distributions. But while OpenShift provides several layers of abstraction over vanilla Kubernetes, this software can quickly become overwhelming because of its rich feature set and functionality. This practical book helps you understand and manage OpenShift clusters from minimal deployment to large multicluster installations.Principal site reliability engineers Rick Rackow and Manuel Dewald, who worked together on Red Hat's managed OpenShift offering for years, provide valuable advice to help your teams operate OpenShift clusters efficiently. Designed for SREs, system administrators, DevOps engineers, and cloud architects, Operating OpenShift encourages consistent and easy container orchestration and helps reduce the effort of deploying a Kubernetes platform. You'll learn why OpenShift has become highly attractive to enterprises large and small.Learn OpenShift core concepts and deployment strategiesExplore multicluster OpenShift Container Platform deploymentsAdminister OpenShift clusters following best practicesLearn best practices for deploying workloads to OpenShiftMonitor OpenShift clusters through state-of-the-art concepts

More organizations than ever understand the importance of data lake architectures for deriving value from their data. Building a robust, scalable, and performant data lake remains a complex proposition, however, with a buffet of tools and options that need to work together to provide a seamless end-to-end pipeline from data to insights.This book provides a concise yet comprehensive overview on the setup, management, and governance of a cloud data lake. Author Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance.Learn the benefits of a cloud-based big data strategy for your organizationGet guidance and best practices for designing performant and scalable data lakesExamine architecture and design choices, and data governance principles and strategiesBuild a data strategy that scales as your organizational and business needs increaseImplement a scalable data lake in the cloudUse cloud-based advanced analytics to gain more value from your data

Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Because rather than simply report what has happened, GA4's new cloud integrations enable more data activation—linking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations.Author Mark Edmondson, Google Developer Expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get guidance on how to implement them.You'll learn:How Google Cloud integrates with GA4The potential use cases that GA4 integrations can enableSkills and resources needed to create GA4 integrationsHow much GA4 data capture is necessary to enable use casesThe process of designing dataflows from strategy though data storage, modeling, and activation

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP.Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way.You'll learn how to:Employ best practices in building highly scalable data and ML pipelines on Google CloudAutomate and schedule data ingest using Cloud RunCreate and populate a dashboard in Data StudioBuild a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQueryConduct interactive data exploration with BigQueryCreate a Bayesian model with Spark on Cloud DataprocForecast time series and do anomaly detection with BigQuery MLAggregate within time windows with DataflowTrain explainable machine learning models with Vertex AIOperationalize ML with Vertex AI Pipelines

Take advantage of the power of cloud and the latest AI techniques. Whether you're an experienced developer wanting to improve your app with AI-powered features or you want to make a business process smarter by getting AI to do some of the work, this book's got you covered. Authors Anand Raman, Chris Hoder, Simon Bisson, and Mary Branscombe show you how to build practical intelligent applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.This book shows you how cloud AI services fit in alongside familiar software development approaches, walks you through key Microsoft AI services, and provides real-world examples of AI-oriented architectures that integrate different Azure AI services. All you need to get started is a working knowledge of basic cloud concepts.Become familiar with Azure AI offerings and capabilitiesBuild intelligent applications using Azure Cognitive ServicesTrain, tune, and deploy models with Azure Machine Learning, PyTorch, and the Open Neural Network Exchange (ONNX)Learn to solve business problems using AI in the Power PlatformUse transfer learning to train vision, speech, and language models in minutes

With technological advancements, fast markets, and higher complexity of systems, software engineers tend to skip the uncomfortable topic of software efficiency. However, tactical, observability-driven performance optimizations are vital for every product to save money and ensure business success. With this book, any engineer can learn how to approach software efficiency effectively, professionally, and without stress. Author Bartłomiej Płotka provides the tools and knowledge required to make your systems faster and less resource-hungry. Efficient Go guides you in achieving better day-to-day efficiency using Go. In addition, most content is language-agnostic, allowing you to bring small but effective habits to your programming or product management cycles. This book shows you how to: Clarify and negotiate efficiency goalsOptimize efficiency on various levelsUse common resources like CPU and memory effectivelyAssess efficiency using observability signals like metrics, logging, tracing, and (continuous) profiling via open source projects like Prometheus, Jaeger, and ParcaApply tools like go test, pprof, benchstat, and k6 to create reliable micro and macro benchmarksEfficiently use Go and its features like slices, generics, goroutines, allocation semantics, garbage collection, and more!

Kubernetes has become the operating system of today's cloud native world, providing a reliable and scalable platform for running containerized workloads. In this friendly, pragmatic book, cloud experts Justin Domingus and John Arundel show you what Kubernetes can do-and what you can do with it. This updated second edition guides you through the growing Kubernetes ecosystem and provides practical solutions to everyday problems with software tools currently in use. You'll walk through an example containerized application running in Kubernetes step-by-step, from the development environment through the continuous deployment pipeline, exploring patterns you can use for your own applications. Make your development teams lean, fast, and effective by adopting Kubernetes and DevOps principles.Understand containers and Kubernetes-no experience necessaryRun your own applications on managed cloud Kubernetes services or on-prem environmentsDesign your own cloud native services and infrastructureUse Kubernetes to manage resource usage and the container lifecycleOptimize clusters for cost, performance, resilience, capacity, and scalabilityLearn the best tools for developing, testing, and deploying your applicationsApply the latest industry practices for observability and monitoringSecure your containers and clusters in production