[Big Data China and Hong Kong]
Big data is a collection of data sets with sizes beyond the ability of commonly used software tools to process, within a tolerable elapsed time. The challenges include
Examples of Big Data include large-scale e-commerce, global financial data, Internet text and documents, RFID, web logs, medical records, photography archives, video archives, Big Science, sensor networks, social networks & social data, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research.
Big data therefore requires exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. Technologies being applied to big data include massively parallel-processing databases, distributed file systems, search-based applications, data-mining grids, cloud based infrastructure, and the Internet.
What is considered "big data" varies depending on the capabilities of the organization managing the set, and on the capabilities of the applications that are traditionally used to process and analyze the data set in its domain. Organizations, when facing difficulty to process large data sets within tolerable elapsed times for the first time, may trigger a need to reconsider data management options, Hadoop is becoming the de facto standard framework of Big Data processing, a fundamentally new way of storing and processing large data sets to reveal insight from all types of data, it is the solution to answer big questions.
The Key Advantages of Hadoop and HBase
Hadoop is the popular data storage and big data analysis platform. Large and successful companies are using it to do powerful analysis. Hadoop offers two important services: It can store any kind of data from any source, inexpensively and at very large scale, and it can do very sophisticated analysis of that data easily and quickly. Hadoop and HBase delivers several key advantages:
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