.NET 4.5 Extension Methods on IQueryable

In this article by Shawn R. McLean, author of Instant .NET 4.5 Extension Methods How-to , we learn about the extension methods on IQueryable. IQueryable is used to operate mainly on databases. IQueryable<T>are an extension from IEnumerable<T>, hence, we can call all extensions and methods of IEnumerable<T>. A query using IQueryable can be built up on over time, before it hits the database. The query is executed once you execute an eager function such as ToList(), looping the data or attempting to use the values. IQueryable is used by providers such as LINQ to entities or LINQ to SQL.

What is OpenLayers?

In this article by Alessio Di Lorenzo and Giovanni Allegri, the authors of the book Instant OpenLayers Starter, we will discuss some basic points about OpenLayers

OpenLayers is an open source map viewing library, originally developed and released by MetaCarta under a BSD license. It is written in pure JavaScript and makes it easy to incorporate interactive maps from a variety of sources into your web pages and applications.

Android Native Application API

Thanks to the Android native application APIs, it is possible to write an Android application with pure native code since Android API level 9 (Android 2.3, Gingerbread). That is, not a single line of Java code is needed. The Android native APIs are defined in several header files under the <NDK root>/platforms/android-<API level>/arch-arm/usr/ include/android/ folder.

In this article by Feipeng Liu author of Android Native Development Kit Cookbook, we will cover the following recipes:

• Creating a native activity with the native_activity.h interface

• Creating a native activity with the Android native app glue

• Managing native windows at Android NDK

• Detecting and handling input events at Android NDK

• Accessing sensors at Android NDK

• Managing assets at Android NDK

Cloud-enabling Your Apps

This article by Darren Cope, author of Appcelerator Titanium Application Development by Example Beginner's Guide will show how to integrate with the cloud and will concentrate on how to use some of the storage-based solutions. Specifically you will learn how to:

• Integrate with Appcelerator Cloud Services
• Interface to a REST-based cloud service
• Send and receive data from the cloud

Echo Server/Clients

In this article by John Torjo, author of Boost.Asio C++ Network Programming, we'll implement a small client/server application, which is probably the easiest client/server application you will ever write. This is the Echo application, a server that echoes back anything a client writes, and then closes the client's connection. The server can handle any number of clients. As each client connects, it sends a message. The server receives the full message and sends it back. After that, it closes the connection.

Therefore, each Echo client connects to the server, sends a message, and reads what the server replies, making sure it's the same message it sent and finishes talking to the server.

Big Data Analysis

This article created by Jonathan R. Owens, Jon Lentz, and Brian Femiano, authors of Hadoop Real-World Solutions Cookbook, contains recipes designed to show how you can put Hadoop to use to answer different questions about your data. Several of the Hive examples will demonstrate how to properly implement and use a custom function (UDF) for reuse in different analytics. There are two Pig recipes that show different analytics with the Audioscrobbler dataset and one MapReduce Java API recipe that shows Combiners.

In this article, we will cover:

• Counting distinct IPs in weblog data using MapReduce and Combiners

• Using Hive date UDFs to transform and sort event dates from geographic event data

• Using Hive to build a per-month report of fatalities over geographic event data

• Implementing a custom UDF in Hive to help validate source reliability over geographic event data

• Marking the longest period of non-violence using Hive MAP/REDUCE operators and Python

• Calculating the cosine similarity of Artists in the Audioscrobbler dataset using Pig

• Trim outliers from the Audioscrobbler dataset using Pig and datafu

Learning to apply Apache Hive, Pig, and MapReduce to solve the specific problems you are faced with can be difficult. The recipes in this article present a few big data problems and provide solutions that show how to tackle them. You will notice that the questions we ask of the data are not incredibly complicated, but you will require a different approach when dealing with a large volume of data. Even though the sample datasets in the recipes are small, you will find that the code is still very applicable to bigger problem spaces distributed over large Hadoop clusters.

The analytic questions in this article are designed to highlight many of the more powerful features of the various tools. You will find many of these features and operators useful as you begin solving your own problems.