Ready to unlock the power of your data? With this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters.
In classical data warehousing terms, organizing data is called data integration. Because there is
such a high volume of big data, there is a tendency to organize data at its original storage
location, thus saving both time and money by not moving around large volumes of data. The
infrastructure required for organizing big data must be able to process and manipulate data in the
original storage location; support very high throughput (often in batch) to deal with large data
processing steps; and handle a large variety of data formats, from unstructured to structured.
Oracle Big Data Appliance brings Big Data solutions to mainstream enterprises. Built using
industry-standard hardware from Sun and Cloudera’s distribution including Apache Hadoop, the
Big Data Appliance is designed and optimized for big data workloads. By integrating the key
components of a big data platform into a single product, Oracle Big Data Appliance delivers an
affordable, scalable and fully supported big data infrastructure without the risks of a custom built
We propose a set of open-source software modules to perform structured Perceptron Training, Prediction and Evaluation within the Hadoop framework. Apache Hadoop is a freely available environment for running distributed applications on a computer cluster. The software is designed within the Map-Reduce paradigm. Thanks to distributed computing, the proposed software reduces substantially execution times while handling huge data-sets. The distributed Perceptron training algorithm preserves convergence properties, thus guaranties same accuracy performances as the serial Perceptron. ...
This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. With Pig, you can batch-process data without having to create a full-fledged application—making it easy for you to experiment with new datasets.
Programming Pig introduces new users to Pig, and provides experienced users with comprehensive coverage on key features such as the Pig Latin scripting language, the Grunt shell, and User Defined Functions (UDFs) for extending Pig. ...