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.

Converting tables into graphs (Advanced)

Another alternative much discussed by the community of developers is transforming the table into a graphic when it is being displayed on small screen devices. This is not an easy task taking into account the size and amount of data that a table can have.

Let's see an alternative solution combining the previous recipes with another plugin for rendering graphics. The main reason for this combination is we use only one plugin per page, thus optimizing our load.

This article by Fernando Monteiro, the author of the book, Instant HTML5 Responsive Table Design How-to explains what happens when we convert the data and display a nice graphic for our users using a properly formatted table.

Querying and Selecting Data

Selecting features from a geographic layer or rows from a standalone attribute table is one of the most common GIS operations. Queries are created to enable these selections, and can be either attribute or spatial queries. Attribute queries use SQL statements to select features or rows through the use of one or more fields or columns in a dataset. An example attribute query would be "Select all land parcels with a property value greater than $500,000". Spatial queries are used to select features based on some type of spatial relationship. An example might be "Select all land parcels that intersect a 100 year floodplain" or perhaps "Select all streets that are completely within Travis County, Texas". It is also possible to combine attribute and spatial queries. An example might be "Select all land parcels that intersect the 100 year floodplain and have a property value greater than $500,000".

In this article by Eric Pimpler, author of Programming ArcGIS 10.1 with Python Cookbook, we will cover the following recipes:

• Constructing proper attribute query syntax

• Creating feature layers and table views

• Selecting features and rows with the Select Layer by Attribute tool

• Selecting features with the Select by Location tool

• Combining spatial and attribute queries with the Select by Location tool

Getting Started with PrimeFaces

The purpose of the article, PrimeFaces—the rapidly evolving, JSF component suite, is to address a wide audience interested in modern, trend-setting Java or JEE web development. In this article by Mert Çalışkan and Oleg Varaksin, authors of PrimeFaces Cookbook, we will be provided with a sneak preview on some of the prime features of PrimeFaces, such as the AJAX processing mechanism and resource handling with Internationalization and Localizaiton, along with the necessary steps to implement a simple web application using PrimeFaces, which will give a head start to the user.

In this article  we will cover:

• Setting up and configuring the PrimeFaces library

• AJAX basics with Process and Update

• Internationalization (i18n) and Localization (L10n)

• Improved resource ordering

• PrimeFaces scaffolding with Spring Roo

Learning to Fly with Force.com

In this article by Ankit Arora and Abhinav Gupta, the authors of the book Force.com Tips and Tricks, we will focus on the basics of cloud computing and briefly go over the following:

• Principles and constructs of Force.com

• Benefits and building blocks of Force.com

• When to choose this platform

• Which edition is right for you?

• A cursory overview of how to manage your Salesforce.com org

Advanced Hadoop MapReduce Administration

In this article by Srinath Perera and Thilina Gunarathne, authors of Hadoop MapReduce Cookbook, we will cover:

• Tuning Hadoop configurations for cluster deployments

• Running benchmarks to verify the Hadoop installation

• Reusing Java VMs to improve the performance

• Fault tolerance and speculative execution

• Debug scripts – analyzing task failures

• Setting failure percentages and skipping bad records

• Shared-user Hadoop clusters – using fair and other schedulers

• Hadoop security – integrating with Kerberos

• Using the Hadoop Tool interface