Big Data Storage and Data Virtualization

April 1, 2019

The big data concept has been adopted by all kinds of organizations. Data can be "big" because of its enormous volume, because it's not structured in the traditional way, or because it's streaming in with enormous quantities. The business advantages of big data systems are clear, they can improve, deepen, and strengthen an organization's analytical capabilities with relatively attractive infrastructure economics.Until now the data virtualization servers have focused on making big data processing easy. They can hide the complex and technical interfaces of big data storage technologies, such as Hadoop and NoSQL, and they can present big data as if it's stored in traditional SQL systems. This allows developers to use their existing skills and to deploy their traditional ETL, reporting, and analytical tools that all support SQL. Additionally, the products and existing skills can extend the data security mechanisms for accessing and processing big data across multiple big data systems. Butcwith scale and performance rising, making big data processing easy is not enough anymore. As such, the next challenge for data virtualization is parallel big data processing.

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“Remember the wine they drank last, on their favourite table by the window” - Building Customer Profiling for Restaurants and a specialised CRM to target their customers better. Realbox Data Analytics uses business intelligence and predictive analysis tools to help offline businesses increase revenues and decrease risk. Their signature product, Pulse, provides decision- makers business summaries and key performance metrics on their mobile devices in real time, helping them remain agile and in control of their businesses.

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