Computer Science, Machine Learning and Big Data

In order to have the competitive advantage, organizations worldwide are driving the need for better analytics (historical, realtime, predictive and cognitive) of data across various domains including customers, products, services and operations. Due to this, the data available for such analytics is exploding in size, technology and complexity. For many years companies have invested in technologies like data warehouses, data marts, OLAP tools, Big Data/Hadoop systems and streaming real-time analytics platforms to take advantage of these opportunities. Total value preposition to the business is maximized only when these are combined into an integrated analytics platform. However, traditional tools cannot integrate streaming data and dataat-rest especially when the data is spread on-premises, cloud, websites and documents everywhere. Data virtualization can be used to provide cross platform logical views of data and analytic insights across the enterprise to provide an integrated analytics platform. By utilizing native integration with in-memory data grids for data processing, data virtualization can deliver a unified and centralized data services fabric with security and real-time integration across multiple traditional and big data sources, including Hadoop, NoSQL, cloud and software-as-a-service (SaaS). Hence data virtualization is becoming a need to address the unique challenges of data explosion in today’s changing business climate.