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Packaged Elegance

In this article by John Farrar , author of the book KnockoutJS Web development , we will see how templates drove us to a more dynamic, creative platform. The next advancement in web development was custom HTML components. KnockoutJS allows us to jump right in with some game-changing elegance for designers and developers. In this article, we will focus on: An introduction to components Bring Your Own Tags ( BYOT ) Enhancing attribute handling Making your own libraries Asynchronous module definition ( AMD )—on demand resource loading This entire article is about packaging your code for reuse. Using these techniques, you can make your code more approachable and elegant.

Speeding Vagrant Development With Docker

In this article by Chad Thompson , author of Vagrant Virtual Development Environment Cookbook , we will learn that many software developers are familiar with using Vagrant ( to distribute and maintain development environments. In most cases, Vagrant is used to manage virtual machines running in desktop hypervisor software such as VirtualBox or the VMware Desktop product suites. (VMware Fusion for OS X and VMware Desktop for Linux and Windows environments.) More recently, Docker ( has become increasingly popular for deploying containers—Linux processes that can run in a single operating system environment yet be isolated from one another. In practice, this means that a container includes the runtime environment for an application, down to the operating system level. While containers have been popular for deploying applications, we can also use them for desktop development. Vagrant can use Docker in a couple of ways: As a target for running a process defined by Vagrant with the Vagrant provider. As a complete development environment for building and testing containers within the context of a virtual machine. This allows you to build a complete production-like container deployment environment with the Vagrant provisioner. In this example, we'll take a look at how we can use the Vagrant provider to build and run a web server. Running our web server with Docker will allow us to build and test our web application without the added overhead of booting and provisioning a virtual machine.

Performance Considerations

In this article by Dayong Du , the author of Apache Hive Essentials , we will look at the different performance considerations when using Hive. Although Hive is built to deal with big data, we still cannot ignore the importance of performance. Most of the time, a better Hive query can rely on the smart query optimizer to find the best execution strategy as well as the default setting best practice from vendor packages. However, as experienced users, we should learn more about the theory and practice of performance tuning in Hive, especially when working in a performance-based project or environment. We will start from utilities available in Hive to find potential issues causing poor performance. Then, we introduce the best practices of performance considerations in the areas of queries and job.

SciPy for Signal Processing

In this article by Sergio J. Rojas G. and Erik A Christensen , authors of the book Learning SciPy for Numerical and Scientific Computing , Second Edition , we will focus on the usage of some most commonly used routines that are included in SciPy modules— scipy.signal , scipy.ndimage , and scipy.fftpack , which are used for signal processing, multidimensional image processing, and computing Fourier transforms, respectively. We define a signal as data that measures either a time-varying or spatially varying phenomena. Sound or electrocardiograms are excellent examples of time-varying quantities, while images embody the quintessential spatially varying cases. Moving images are treated with the techniques of both types of signals, obviously. The field of signal processing treats four aspects of this kind of data: its acquisition, quality improvement, compression, and feature extraction. SciPy has many routines to treat effectively tasks in any of the four fields. All these are included in two low-level modules ( scipy.signal being the main module, with an emphasis on time-varying data, and scipy.ndimage , for images). Many of the routines in these two modules are based on Discrete Fourier Transform of the data. SciPy has an extensive package of applications and definitions of these background algorithms, scipy.fftpack , which we will start covering first.

Central Air and Heating Thermostat

 In this article by Andrew K. Dennis , author of the book Raspberry Pi Home Automation with Arduino Second Edition , you will learn how to build a thermostat device using an Arduino. You will also learn how to use the temperature data to switch relays on and off. Relays are the main components that you can use for interaction between your Arduino and high-voltage electronic devices. The thermostat will also provide a web interface so that you can connect to it and check out the temperature.

Elasticsearch Administration

In this article by Rafał Kuć and Marek Rogoziński , author of the book Mastering Elasticsearch, Second Edition we will talk more about the Elasticsearch configuration and new features introduced in Elasticsearch 1.0 and higher. By the end of this article, you will have learned:

A Quick Start Guide to Flume

In this article by Steve Hoffman , the author of the book, Apache Flume: Distributed Log Collection for Hadoop Second Edition , we will learn about the basics that are required to be known before we start working with Apache Flume. This article will help you get started with Flume. So, let's start with the first step: downloading and configuring Flume.

Entity Framework DB First – Inheritance Relationships between Entities

This article is written by Rahul Rajat Singh , the author of Mastering Entity Framework . So far, we have seen how we can use various approaches of Entity Framework, how we can manage database table relationships, and how to perform model validations using Entity Framework. In this article, we will see how we can implement the inheritance relationship between the entities. We will see how we can change the generated conceptual model to implement the inheritance relationship, and how it will benefit us in using the entities in an object-oriented manner and the database tables in a relational manner.

Building a Color Picker with Hex RGB Conversion

 In this article by Vijay Joshi , author of the book Mastering jQuery UI , we are going to create a color selector, or color picker, that will allow the users to change the text and background color of a page using the slider widget. We will also use the spinner widget to represent individual colors. Any change in colors using the slider will update the spinner and vice versa. The hex value of both text and background colors will also be displayed dynamically on the page. This is how our page will look after we have finished building it:

Dealing with Interrupts

 This article is written by Francis Perea , the author of the book Arduino Essentials . In all our previous projects, we have been constantly looking for events to occur. We have been polling, but looking for events to occur supposes a relatively big effort and a waste of CPU cycles to only notice that nothing happened. In this article, we will learn about interrupts as a totally new way to deal with events, being notified about them instead of looking for them constantly. Interrupts may be really helpful when developing projects in which fast or unknown events may occur, and thus we will see a very interesting project which will lead us to develop a digital tachograph for a computer-controlled motor. Are you ready? Here we go!

Starting Small and Growing in a Modular Way

This article written by Carlo Russo , author of the book KnockoutJS Blueprints , describes that RequireJS gives us a simplified format to require many parameters and to avoid parameter mismatch using the CommonJS require format; for example, another way (use this or the other one) to write the previous code is: define(function(require) {    var $ = require("jquery"),        ko = require("knockout"),        viewModel = {};    $(function() {        ko.applyBindings(viewModel);    }); }); In this way, we skip the dependencies definition, and RequireJS will add all the texts require('xxx') found in the function to the dependency list. The second way is better because it is cleaner and you cannot mismatch dependency names with named function arguments. For example, imagine you have a long list of dependencies; you add one or remove one, and you miss removing the relative function parameter. You now have a hard-to-find bug. And, in case you think that r.js optimizer behaves differently, I just want to assure you that it's not so; you can use both ways without any concern regarding optimization. Just to remind you, you cannot use this form if you want to load scripts dynamically or by depending on variable value; for example, this code will not work: var mod = require(someCondition ? "a" : "b"); if (someCondition) {    var a = require('a'); } else {    var a = require('a1'); } You can learn more about this compatibility problem at this URL: . You can see more about this sugar syntax at this URL: . Now that you know the basic way to use RequireJS, let's look at the next concept.


 In this article, by Einar Ingebrigtsen , author of the book, SignalR Blueprints , we will focus on a different programming model for client development: Model-View-ViewModel ( MVVM ). It will reiterate what you have already learned about SignalR, but you will also start to see a recurring theme in how you should architect decoupled software that adheres to the SOLID principles. It will also show the benefit of thinking in single page application terms (often referred to as Single Page Application ( SPA )), and how SignalR really fits well with this idea.

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