Intel has unveiled a software based on open source technologies called the Intel Data Platform. It is designed to make it easier and faster for companies to make big analysis.
The Intel Data Platform: Analytics Toolkit (Intel Data Platform AT) will create a graph analytics and predictive modelling environment to help businesses uncover valuable insights from hidden relationships within data.
The software gives an open environment for import,management and analysis of big data that builds upon the Intel Distribution for Apache Hadoop to offer improved reliability and enterprise-class security and support; and features a number of features including streaming data processing, interactive and iterative analytics, and graph processing.
“As big data shifts from hype to reality, Intel is helping to break down the barriers to adoption by easing complexity and creating more value,” says Boyd Davis, vice-president and GM of Intel’s Datacenter Software Division. “Much like an operating system for big data processing, the Intel Data Platform supports a wide variety of applications while providing improved security, reliability and peace of mind to customers using open source software.”
Having integrates frameworks for stream processing in real time,companies such as retailers can use the Intel Data Platform to analyse social media and sensor data, in-store purchases, and inventory to gauge the impact of a celebrity endorsement on the demand for a new product.
Using the interactive and iterative frameworks for big data applications, industries such as telecommunications can look up data tailored to a specific customer in order to personalize recommendations for new products or services.
The platform will be available next quarter in Enterprise Edition and Premium Edition versions each offering various levels
of support.
As for the Enterprise Edition, it will full platform capabilities as a free software product to customers who can support
their deployment and Premium Edition, available for purchase on an annual subscription basis and will provide additional technical features.
The toolkit provides a foundation of common algorithms, that IT teams can build on and customise with domain-specific code.
The toolkit also provides an enhanced development framework for unifying graph analytics and classical machine learning to ease the programming effort.
