IBM announced the Integrated Analytics System, a new unified data system designed to give users fast, easy access to advanced data science capabilities and the ability to work with their data across private, public or hybrid cloud environments.
The system, which comes with a variety of data science tools built-in, allows data scientists to get up and running quickly to develop and deploy their advanced analytics models in-place, directly where the data resides for greater performance. It is based on the IBM common SQL engine, so clients can use the system to easily move workloads to the public cloud to begin automating their businesses with machine learning.
At the heart of the system are the IBM Data Science Experience, Apache Spark and the Db2 Warehouse, all of which have been optimized to work together with straight forward management. The Data Science Experience provides a set of critical data science tools and a collaborative work space through which data scientists can create new analytic models that developers can use to build intelligent applications quickly and easily. The inclusion of Apache Spark enables in-memory data processing, which speeds analytic applications by allowing analytics to be processed directly where the data resides.
New to this class of offering are the machine learning capabilities that come with both the Data Science Experience and Spark embedded on the system. Having machine learning processing embedded means that data does not need to be moved to the analytics processing, reducing the associated processes and wait times for analytics to run and respond. This simplifies the process of training and evaluating predictive models, as well as the testing, deployment and training as it is all done in-place.
The integrated architecture of the new system combines software enhancements such as asymmetric massively parallel processing (AMPP) with IBM Power technology and flash memory storage hardware and builds on the PureData System for Analytics, and the previous Netezza data warehouse offerings. It also supports a wide range of data types and data services, including everything from the Watson Data Platform and Db2 Warehouse On Cloud, to Hadoop and BigSQL. In addition, industry standard tools and the common SQL engine provide users with an option to also move these workloads seamlessly to public or private cloud environments with Spark clusters, based on the user’s requirements.
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