Selenium Framework Design in Data-Driven Testing. Build data-driven test frameworks using Selenium WebDriver, AppiumDriver, Java, and TestNG

Carl Cocchiaro

The Selenium WebDriver 3.x Technology is an open source API available to test both Browser and Mobile applications. It is completely platform independent in that tests built for one browser or mobile device, will also work on all other browsers and mobile devices. Selenium supports all major development languages which allow it to be tied directly into the technology used to develop the applications. This guide will provide a step-by-step approach to designing and building a data-driven test framework using Selenium WebDriver, Java, and TestNG.The book starts off by introducing users to the Selenium Page Object Design Patterns and D.R.Y Approaches to Software Development. In doing so, it covers designing and building a Selenium WebDriver framework that supports both Browser and Mobile Devices. It will lead the user through a journey of architecting their own framework with a scalable driver class, Java utility classes, JSON Data Provider, Data-Driven Test Classes, and support for third party tools and plugins.Users will learn how to design and build a Selenium Grid from scratch to allow the framework to scale and support different browsers, mobile devices, versions, and platforms, and how they can leverage third party grids in the Cloud like SauceLabs.Other topics covered include designing abstract base and sub-classes, inheritance, dual-driver support, parallel testing, testing multi-branded applications, best practices for using locators, and data encapsulation.Finally, you will be presented with a sample fully-functional framework to get them up and running with the Selenium WebDriver for browser testing.By the end of the book, you will be able to design your own automation testing framework and perform data-driven testing with Selenium WebDriver.

Full Stack Development with JHipster. Build modern web applications and microservices with Spring and Angular

Deepu K Sasidharan, Sendil Kumar Nellaiyapen

JHipster is a development platform to generate, develop, and deploy Spring Boot and Angular/React applications and Spring microservices. It provides you with a variety of tools that will help you quickly build modern web applications. This book will be your guide to building full stack applications with Spring and Angular using the JHipster tool set.You will begin by understanding what JHipster is and the various tools and technologies associated with it. You will learn the essentials of a full stack developer before getting hands-on and building a monolithic web application with JHipster. From here you will learn the JHipster Domain Language with entity modeling and entity creation using JDL and JDL studio. Moving on, you will be introduced to client side technologies such as Angular and Bootstrap and will delve into technologies such as Spring Security, Spring MVC, and Spring Data. You will learn to build and package apps for production with various deployment options such as Heroku and more. During the course of the book, you will be introduced to microservice server-side technologies and how to break your monolithic application with a database of your choice. Next, the book takes you through cloud deployment with microservices on Docker and Kubernetes. Going forward, you will learn to build your client side with React and master JHipster best practices.By the end of the book, you will be able to leverage the power of the best tools available to build modern web applications.

Scala and Spark for Big Data Analytics. Explore the concepts of functional programming, data streaming, and machine learning

Md. Rezaul Karim, Sridhar Alla

Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you.The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment.You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio.By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.

SQL Server 2017 Developer's Guide. A professional guide to designing and developing enterprise database applications

Dejan Sarka, William Durkin, Milo?° Radivojevifá

Microsoft SQL Server 2017 is a milestone in Microsoft's data platform timeline, as it brings in the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. This book prepares you for advanced topics by starting with a quick introduction to SQL Server 2017's new features. Then, it introduces you to enhancements in the Transact-SQL language and new database engine capabilities before switching to a different technology: JSON support. You will take a look at the security enhancements and temporal tables. Furthermore, the book focuses on implementing advanced topics, including Query Store, columnstore indexes, and In-Memory OLTP. Toward the end of the book, you'll be introduced to R and how to use the R language with Transact-SQL for data exploration and analysis. You'll also learn to integrate Python code into SQL Server and graph database implementations as well as the deployment options on Linux and SQL Server in containers for development and testing.By the end of this book, you will be armed to design efficient, high-performance databaseapplications without any hassle.

Effective Amazon Machine Learning. Expert web services for machine learning on cloud

Alexis Perrier

Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection.This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets.

Hands-On Neural Networks with Keras. Design and create neural networks using deep learning and artificial intelligence principles

Niloy Purkait

Neural networks are used to solve a wide range of problems in different areas of AI and deep learning. Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks. By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization.

Mastering Machine Learning with R. Advanced prediction, algorithms, and learning methods with R 3.x - Second Edition

Cory Lesmeister

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more.You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do.With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.

Learning ServiceNow. Get started with ServiceNow administration and development to manage and automate your IT Service Management processes

Tim Woodruff, Sylvain Hauser

This book shows you how to put important ServiceNow features to work in the real world. We will introduce key concepts and examples on managing and automating IT services, and help you build a solid foundation towards this new approach. We’ll demonstrate how to effectively implement various system configurations within ServiceNow. We’ll show you how to configure and administer your instance, and then move on to building strong user interfaces and creating powerful workflows. We also cover other key elements of ServiceNow, such as alerts and notifications, security, reporting, and custom development. You will learn how to improve your business’ workflow, processes, and operational efficiency. By the end of this book, you will be able to successfully configure and manage ServiceNow within your organization.