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Modern Java EE Design Patterns
Modern Java EE Design Patterns

With the ascent of DevOps, microservices, containers, and cloud-based development platforms, the gap between state-of-the-art solutions and the technology that enterprises typically support has greatly increased. But as Markus Eisele explains in this O'Reilly report, some enterprises are now looking to bridge that gap by building microservice-based architectures on top of Java EE. Can it be done? Is it even a good idea? Eisele thoroughly explores the possibility and provides savvy advice for enterprises that want to move ahead. The issue is complex: Java EE wasn't built with the distributed application approach in mind, but rather as one monolithic server runtime or cluster hosting many different applications. If you're part of an enterprise development team investigating the use of microservices with Java EE, this book will help you: - Understand the challenges of starting a greenfield development vs tearing apart an existing brownfield application into services; - Examine your b ...
Beginning Azure Static Web Apps
Beginning Azure Static Web Apps

Create rich and dynamic web applications on the Azure cloud platform using static web development techniques built around Blazor WebAssembly, APIs, and Markup, while leveraging the paradigm commonly known as JAMstack. This book starts off showing you how to create an environment for deploying your first application. You will create an Azure Static Web App using a Blazor WebAssembly application and adding dynamic content using an Azure function before deploying from GitHub. You will learn to debug your Static Web App locally, both inside of Visual Studio and from the command line using a simple Static Web App CLI command. The book takes a deep dive into the CLI to allow you to emulate all of the features available in the Azure environment. You will learn the authentication and authorizing options with your app and create new blog posts with the post creation function. Included in the book is setting a custom domain and discussion of the options. The book also explores the differences ...
Pro Data Mashup for Power BI
Pro Data Mashup for Power BI

This book provides all you need to find data from external sources and load and transform that data into Power BI where you can mine it for business insights and a competitive edge. This ranges from connecting to corporate databases such as Azure SQL and SQL Server to file-based data sources, and cloud- and web-based data sources. The book also explains the use of Direct Query and Live Connect to establish instant connections to databases and data warehouses and avoid loading data. The book provides detailed guidance on techniques for transforming inbound data into normalized data sets that are easy to query and analyze. This covers data cleansing, data modification, and standardization as well as merging source data into robust data structures that can feed into your data model. You will learn how to pivot and transpose data and extrapolate missing values as well as harness external programs such as R and Python into a Power Query data flow. You also will see how to handle errors ...
Effective Haskell
Effective Haskell

Put the power of Haskell to work in your programs, learning from an engineer who uses Haskell daily to get practical work done efficiently. Leverage powerful features like Monad Transformers and Type Families to build useful applications. Realize the benefits of a pure functional language, like protecting your code from side effects. Manage concurrent processes fearlessly. Apply functional techniques to working with databases and building RESTful services. Don't get bogged down in theory, but learn to employ advanced programming concepts to solve real-world problems. Don't just learn the syntax, but dive deeply into Haskell as you build efficient, well-tested programs. Haskell is a pure functional programming language with a rich ecosystem of tools and libraries. Designed to push the boundaries of programming, it offers unparalleled power for building reliable and maintainable systems. But to unleash that power, you need a guide. Effective Haskell is that guide. Written by an engin ...
The Enterprise Path to Service Mesh Architectures
The Enterprise Path to Service Mesh Architectures

Planning to build a microservice-driven cloud native application or looking to modernize existing application services? Consider using a service mesh. A service mesh approach can help you create robust and scalable applications, but it also introduces new challenges. This updated report answers common questions regarding service mesh architectures through the lens of a large enterprise. Author Lee Calcote, founder and CEO of Layer5, shows developers, operators, architects, and IT leaders how to evaluate your organization's readiness for using a service mesh-and provides a clear path to help you adopt one. You'll evaluate several factors when deciding which applications should be built from the ground up and which can be converted with a new service mesh architecture. This updated edition discusses several service meshes available and the tools you need to implement them. You'll explore: - Service mesh concepts, architecture, and components, such as control planes and data planes ...
3D Deep Learning with Python
3D Deep Learning with Python

With this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time. Complete with step-by-step explanations of essential concepts and practical examples, this book lets you explore and gain a thorough understanding of state-of-the-art 3D deep learning. You'll see how to use PyTorch3D for basic 3D mesh and point cloud data processing, including loading and saving ply and obj files, projecting 3D points into camera coordination using perspective camera models or orthographic camera models, rendering point clouds and meshes to images, and much more. As you implement some of the latest 3D deep learning algorithms, such as differential rendering, Nerf, synsin, and mesh RCNN, you'll realize how coding for these deep learning models becomes easier using the PyTorch3D library. By the end of this deep learning book, you'll be ready to implement your own 3D deep learning models confidently. ...
Power Platform and Dynamics 365 CE for Absolute Beginners
Power Platform and Dynamics 365 CE for Absolute Beginners

This is your complete guide to less-code and no-code theories, along with practical application of Microsoft Power Apps and Dynamics 365 CE/CRM Apps. The book covers topics including the configurations, customizations, and enhancements in Microsoft Power Apps and Dynamics 365 CE/CRM Apps. You will start by learning Microsoft Dataverse concepts followed by Microsoft Canvas Apps, model-driven apps, and PowerApps Portals. You will understand how to work with Power Virtual Agent, Power BI, and Power Automate, and how to use AI in Power Apps. The book provides important integration concepts for Power Apps, Dynamics 365 CE/CRM Apps, and Microsoft Azure. You will know how to customize Dynamics 365 CE/CRM Apps and Power Apps using OOTB capabilities. After reading this book, you will understand how Microsoft Power Apps and Dynamics 365 CE/CRM Apps can be used, configured, and customized for your business needs using customer data. You will be able to increase efficiency in customer data m ...
SQL Server 2022 Query Performance Tuning, 6th Edition
SQL Server 2022 Query Performance Tuning, 6th Edition

Troubleshoot slow-performing queries and make them run faster. Database administrators and SQL developers are constantly under pressure to provide more speed. This new edition has been redesigned and rewritten from scratch based on the last 15 years of learning, knowledge, and experience accumulated by the author. The book Includes expanded information on using extended events, automatic execution plan correction, and other advanced features now available in SQL Server. These modern features are covered while still providing the necessary fundamentals to better understand how statistics and indexes affect query performance. The book gives you knowledge and tools to help you identify poorly performing queries and understand the possible causes of that poor performance. The book also provides mechanisms for resolving the issues identified, whether on-premises, in containers, or on cloud platform providers. You'll learn about key fundamentals, such as statistics, data distribution, car ...
Operating OpenShift
Operating OpenShift

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. ...
Machine Learning Engineering on AWS
Machine Learning Engineering on AWS

There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detai ...
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