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Building Python Web APIs with FastAPI
Building Python Web APIs with FastAPI

RESTful web services are commonly used to create APIs for web-based applications owing to their light weight and high scalability. This book will show you how FastAPI, a high-performance web framework for building RESTful APIs in Python, allows you to build robust web APIs that are simple and intuitive and makes it easy to build quickly with very little boilerplate code. This book will help you set up a FastAPI application in no time and show you how to use FastAPI to build a REST API that receives and responds to user requests. You'll go on to learn how to handle routing and authentication while working with databases in a FastAPI application. The book walks you through the four key areas: building and using routes for create, read, update, and delete (CRUD) operations; connecting the application to SQL and NoSQL databases; securing the application built; and deploying your application locally or to a cloud environment. By the end of this book, you'll have developed a solid unde ...
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 ...
Exploring Graphs with Elixir
Exploring Graphs with Elixir

Data is everywhere - it's just not very well connected, which makes it super hard to relate dataset to dataset. Using graphs as the underlying glue, you can readily join data together and create navigation paths across diverse sets of data. Add Elixir, with its awesome power of concurrency, and you'll soon be mastering data networks. Learn how different graph models can be accessed and used from within Elixir and how you can build a robust semantics overlay on top of graph data structures. We'll start from the basics and examine the main graph paradigms. Get ready to embrace the world of connected data! Graphs provide an intuitive and highly flexible means for organizing and querying huge amounts of loosely coupled data items. These data networks, or graphs in math speak, are typically stored and queried using graph databases. Elixir, with its noted support for fault tolerance and concurrency, stands out as a language eminently suited to processing sparsely connected and distribute ...
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. ...
Supercharging Node.js Applications with Sequelize
Supercharging Node.js Applications with Sequelize

Continuous changes in business requirements can make it difficult for programmers to organize business logic into database models, which turns out to be an expensive operation as changes to the database may result in errors and incongruity within applications. Supercharging Node.js Applications with Sequelize helps you get to grips with Sequelize, a reliable ORM that enables you to alleviate these issues in your database and applications. With Sequelize, you'll no longer need to store information in flat files or memory. This book takes a hands-on approach to implementation and associated methodologies for your database that will have you up and running in no time. You'll learn how to configure Sequelize for your Node.js application properly, develop a better sense of understanding of how this ORM works, and find out how to manage your database from Node.js using Sequelize. Finally, you'll be able to use Sequelize as the database driver for building your application from scratch. ...
Learning Tableau 2022, 5th Edition
Learning Tableau 2022, 5th Edition

Learning Tableau 2022 helps you get started with Tableau and data visualization, but it does more than just cover the basic principles. It helps you understand how to analyze and communicate data visually, and articulate data stories using advanced features. This new edition is updated with Tableau's latest features, such as dashboard extensions, Explain Data, and integration with CRM Analytics (Einstein Analytics), which will help you harness the full potential of artificial intelligence (AI) and predictive modeling in Tableau. After an exploration of the core principles, this book will teach you how to use table and level of detail calculations to extend and alter default visualizations, build interactive dashboards, and master the art of telling stories with data. You'll learn about visual statistical analytics and create different types of static and animated visualizations and dashboards for rich user experiences. We then move on to interlinking different data sources wit ...
Microservices with Go
Microservices with Go

This book covers the key benefits and common issues of microservices, helping you understand the problems microservice architecture helps to solve, the issues it usually introduces, and the ways to tackle them. You'll start by learning about the importance of using the right principles and standards in order to achieve the key benefits of microservice architecture. The following chapters will explain why the Go programming language is one of the most popular languages for microservice development and lay down the foundations for the next chapters of the book. You'll explore the foundational aspects of Go microservice development including service scaffolding, service discovery, data serialization, synchronous and asynchronous communication, deployment, and testing. After covering the development aspects, you'll progress to maintenance and reliability topics. The last part focuses on more advanced topics of Go microservice development including system reliability, observability, main ...
Web Development with Julia and Genie
Web Development with Julia and Genie

Julia's high-performance and scalability characteristics and its extensive number of packages for visualizing data make it an excellent fit for developing web apps, web services, and web dashboards. The two parts of this book provide complete coverage to build your skills in web development. First, you'll refresh your knowledge of the main concepts in Julia that will further be used in web development. Then, you'll use Julia's standard web packages and examine how the building blocks of the web such as TCP-IP, web sockets, HTTP protocol, and so on are implemented in Julia's standard library. Each topic is discussed and developed into code that you can apply in new projects, from static websites to dashboards. You'll also understand how to choose the right Julia framework for a project. The second part of the book talks about the Genie framework. You'll learn how to build a traditional to do app following the MVC design pattern. Next, you'll add a REST API to this project, including ...
Software Development with Go
Software Development with Go

Gain insights into the different challenges that can be solved using Go, with a focus on containers, Linux, security, networking, user interfaces and other relevant cloud based topics. This book reviews the necessary tools to create container-based cloud solutions with Go, a programming language that was born out of the need to address scalable, high availability cloud computing architecture needs inside Google. Go, also known as Golang, has been adopted across different industries and products with many popular Open Source projects that power cloud computing technologies such as Docker and Kubernetes being written with Go. As the complexity of cloud technology increases, so does the need for people to understand how things work under-the-hood and to fix them when they're broken. ...
Machine Learning with R, 4th Edition
Machine Learning with R, 4th Edition

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data. You'll also find this classic R data science book updated to R 4.0.0 with newer and better ...
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