Django 2 Web Development Cookbook. 100 practical recipes on building scalable Python web apps with Django 2 - Third Edition

Jake Kronika, Aidas Bendoraitis

Django is a framework designed to balance rapid web development with high performance. It handles high levels of user traffic and interaction, integrates with a variety of databases, and collects and processes data in real time. This book follows a task-based approach to guide you through developing with the Django 2.1 framework, starting with setting up and configuring Docker containers and a virtual environment for your project.You'll learn how to write reusable pieces of code for your models and manage database changes. You'll work with forms and views to enter and list data, applying practical examples using templates and JavaScript together for the optimum user experience. This cookbook helps you to adjust the built-in Django administration to fit your needs and sharpen security and performance to make your web applications as robust, scalable, and dependable as possible. You'll also explore integration with Django CMS, the popular content management suite.In the final chapters, you'll learn programming and debugging tricks and discover how collecting data from different sources and providing it to others in various formats can be a breeze. By the end of the book, you'll learn how to test and deploy projects to a remote dedicated server and scale your application to meet user demands.

Artificial Intelligence with Power BI. Take your data analytics skills to the next level by leveraging the AI capabilities in Power BI

Mary-Jo Diepeveen

The artificial intelligence (AI) capabilities in Power BI enable organizations to quickly and easily gain more intelligent insights from unstructured and structured data.This book will teach you how to make use of the many AI features available today in Power BI to quickly and easily enrich your data and gain better insights into patterns that can be found in your data.You’ll begin by understanding the benefits of AI and how it can be used in Power BI. Next, you’ll focus on exploring and preparing your data for building AI projects and then progress to using prominent AI features already available in Power BI, such as forecasting, anomaly detection, and Q&A. Later chapters will show you how to apply text analytics and computer vision within Power BI reports. This will help you create your own Q&A functionality in Power BI, which allows you to ask FAQs from another knowledge base and then integrate it with PowerApps. Toward the concluding chapters, you’ll be able to create and deploy AutoML models trained in Azure ML and consume them in Power Query Editor. After your models have been trained, you’ll work through principles such as privacy, fairness, and transparency to use AI responsibly.By the end of this book, you’ll have learned when and how to enrich your data with AI using the out-of-the-box AI capabilities in Power BI.

Data Processing with Optimus. Supercharge big data preparation tasks for analytics and machine learning with Optimus using Dask and PySpark

Dr. Argenis Leon, Luis Aguirre

Optimus is a Python library that works as a unified API for data cleaning, processing, and merging data. It can be used for handling small and big data on your local laptop or on remote clusters using CPUs or GPUs.The book begins by covering the internals of Optimus and how it works in tandem with the existing technologies to serve your data processing needs. You'll then learn how to use Optimus for loading and saving data from text data formats such as CSV and JSON files, exploring binary files such as Excel, and for columnar data processing with Parquet, Avro, and OCR. Next, you'll get to grips with the profiler and its data types - a unique feature of Optimus Dataframe that assists with data quality. You'll see how to use the plots available in Optimus such as histogram, frequency charts, and scatter and box plots, and understand how Optimus lets you connect to libraries such as Plotly and Altair. You'll also delve into advanced applications such as feature engineering, machine learning, cross-validation, and natural language processing functions and explore the advancements in Optimus. Finally, you'll learn how to create data cleaning and transformation functions and add a hypothetical new data processing engine with Optimus.By the end of this book, you'll be able to improve your data science workflow with Optimus easily.

Instant Handlebars.js. Learn how to create and implement HTML templates into your projects using the Handlebars library

Gabriel Manricks

Handlebars is one of the leading JavaScript templating engines and is quickly gaining widespread adoption by developers, as well as with frameworks like Ember.js and Meteor.Instant Handlebars.js is a complete guide to the Handlebars library filled with internal concepts and practical examples that will help illustrate what's going on and take you from a complete beginner to a Handlebars expert.Instant Handlebars.js begins with the very basics, requiring no previous knowledge about templating engines. Throughout the course of this book, you get a thorough tour of all the features available in Handlebars.js and you will learn how to organize your websites for both development and production.In this book, we will cover how to create templates with both placeholders and helpers. We will then go into organizing your projects for rapid -development using Require.js and how to optimize and compile your projects for production. To finish off, you will learn how to annotate your code and leave debug points so that you can easily maintain and troubleshoot your code in the future.Handlebars is a small library;, it is meant to fill a specific need and it does this well. Instant Handlebars.js takes a very methodical approach to cover every aspect of this amazing library with practical examples provided every step of the way.

DynamoDB Applied Design Patterns. Apply efficient DynamoDB design patterns for high performance of applications

Uchit Hamendra Vyas

Microservices with Go. Building scalable and reliable microservices with Go

Alexander Shuiskov

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, maintainability, and scalability. In this part, you’ll dive into the best practices and examples which illustrate how to apply the key ideas to existing applications, using the services scaffolded in the previous part as examples.By the end of this book, you’ll have gained hands-on experience with everything you need to develop scalable, reliable and performant microservices using Go.

Jasmine Cookbook. Over 35 recipes to design and develop Jasmine tests to produce world-class JavaScript applications

Munish Kumar

Python Parallel Programming Cookbook. Master efficient parallel programming to build powerful applications using Python

Giancarlo Zaccone

This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will learnGPU programming withPython using the PyCUDA module along with evaluating performance limitations.