IBM today announced the Integrated Analytics System, a new unified data system designed to give users fast access to 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. And because it is based on the IBM common SQL engine, clients can use the system to easily move workloads to the public cloud to begin automating their businesses with machine learning. In fact, because the popular database engine is used across both hosted and cloud-based databases, users can move and query data across multiple data stores, such as the Db2 Warehouse on Cloud, or Hortonworks Data Platform.
At the heart of the Integrated Analytics 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. The inclusion of Apache Spark, the popular open source framework, 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.
The integrated architecture of the new system combines software enhancements such as asymmetric parallel processing (AMPP) with IBM Power technology and flash memory storage hardware and builds on the IBM PureData System for Analytics, and the previous IBM Netezza data warehouse offerings. It also supports a wide range of data types and data services, including everything from the Watson Data Platform and IBM Db2 Warehouse On Cloud, to Hadoop and IBM BigSQL. Like these solutions, the Integrated Analytics System is built with the IBM common SQL engine, enabling users to integrate the unit with cloud-based warehouse solutions.
In addition, industry standard tools and the common SQL engine provide users with an option to also move these workloads to public or private cloud environments with Spark clusters, based on the user's requirements.
Like IBM's existing data warehouse products, the Integrated Analytics System is designed to provide built-in data virtualization and compatibility with Netezza, Db2, and IBM PureData System for Analytics.
Among these capabilities, the new system also incorporates hybrid transactional analytical processing (HTAP). In contrast to typical business environments where transaction processing and analytics are run on distinct architectures, HTAP runs predictive analytics, transactional and historical data on the same database at accelerated response times. Later this year, the company plans to add support for HTAP with IBM Db2 Analytics Accelerator for z/OS, which will enable the system to transparently integrate with IBM z Systems infrastructures.