In this article by Hussein Nasser , author of the book ArcGIS By Example we will discuss the following topics: Geodatabase editing Preparing the data and project Creating excavation features Viewing and editing excavation information
In this article by Frank Appel , author of the book Testing with JUnit , we will learn that special care has to be taken when testing a component's functionality under exception-raising conditions. You'll also learn how to use the various capture and verification possibilities and discuss their pros and cons. As robust software design is one of the declared goals of the test-first approach, we're going to see how tests intertwine with the fail fast strategy on selected boundary conditions. Finally, we're going to conclude with an in-depth explanation of working with collaborators under exceptional flow and see how stubbing of exceptional behavior can be achieved. The topics covered in this article are as follows: Testing patterns Treating collaborators
In the following article by Austin Scott , the author of Learning RSLogix 5000 Programming , you will be introduced to the high performance, asynchronous nature of the Logix family of controllers and the requirement for the buffering I/O module data it drives. You will learn various techniques for the buffering I/O module values in RSLogix 5000 and Studio 5000 Logix Designer . You will also learn about the IEC Languages that do not require the input or output module buffering techniques to be applied to them. In order to understand the need for buffering, let's start by exploring the evolution of the modern line of Rockwell Automation Controllers .
In this article by Marco Schwartz , author of the book Intel Galileo Networking Cookbook , we will cover the recipe, Reading pins via a web server .
In this article by Viktor Farcic and Alex Garcia , the authors of the book Test-Driven Java Development , we will go through TDD in a simple procedure of writing tests before the actual implementation. It's an inversion of a traditional approach where testing is performed after the code is written.
In this article by Henry Garner , author of the book Clojure for Data Science , we'll be working with a relatively modest dataset of only 100,000 records. This isn't big data (at 100 MB, it will fit comfortably in the memory of one machine), but it's large enough to demonstrate the common techniques of large-scale data processing. Using Hadoop (the popular framework for distributed computation) as its case study, this article will focus on how to scale algorithms to very large volumes of data through parallelism. Before we get to Hadoop and distributed data processing though, we'll see how some of the same principles that enable Hadoop to be effective at a very large scale can also be applied to data processing on a single machine, by taking advantage of the parallel capacity available in all modern computers.