Apache Hadoop meets the challenges of Big Data by simplifying the implementation of data-intensive, highly parallel distributed applications. Used throughout the world by businesses, universities, and other organizations, it allows analytical tasks to be divided into fragments of work and distributed over thousands of computers, providing fast analytics time and distributed storage of massive amounts of data.
Hadoop provides a cost-effective way for storing huge quantities of data. It provides a scalable and reliable mechanism for processing large amounts of data over a cluster of commodity hardware. And it provides new and improved analysis techniques that enable sophisticated analytical processing of multi-structured data.
Smart Source enterprise architects understand how the Hadoop frameworks and APIs fit together, and how they can be integrated to deliver real-world solutions.
Architecture For Hadoop
Everyone says it — we are living in the era of "Big Data" Chances are that you have heard this phrase. In today’s technology-fueled world where computing power has significantly increased, electronic devices are more commonplace, accessibility to the Internet has improved, and users have been able to transmit and collect more data than ever before.
What are the challenges with Big Data? How can you store, process, and analyze such a large amount of data to identify patterns and knowledge from a massive sea of information?