Companies combine IBM Data Science Experience and Machine Learning with Hortonworks Data Platform; Companies to propel Apache open source projects with new code
IBM and Hortonworks announced an expansion to their relationship focused on extending data science and machine learning to more developers and across the Apache Hadoop ecosystem. The companies are combining Hortonworks Data Platform (HDP) with IBM Data Science Experience and IBM Big SQL into new integrated solutions designed to help everyone from data scientists to business leaders better analyze and manage their mounting data volumes and accelerate data-driven decision-making.
“The combination of IBM’s data science and Hortonworks’ open and connected data platforms will benefit not only our respective clients, but also the Apache open source community because of our combined investment and collaboration,” said Rob Bearden, CEO, Hortonworks. “We’re excited about the inevitable acceleration in technical innovations that this relationship is being designed to foster, the result being smarter and more agile businesses.”
“This partnership will provide an integrated and open data science and machine learning platform that lets teams easily collaborate and operationalize data science,” said Rob Thomas, General Manager, IBM Analytics. “Incorporating advanced machine learning and deep learning capabilities, the combination of Hortonworks Data Platform with IBM’s Data Science Experience and the IBM Machine Learning platform can help clients achieve improved analytic results faster and at scale.”
The news builds on the long-standing relationship between the companies and includes the following:
- Hortonworks will resell the IBM Data Science Experience with HDP, a leading Hadoop distribution, and adopt it as its strategic data science platform, giving developers a fast on-ramp to data science capabilities including machine learning, advanced analytics and statistics. Also, Hortonworks and IBM will create new solution bundles that integrate HDP with IBM Big SQL, IBM’s SQL engine for Hadoop, giving Hortonworks’ legions of clients and users a familiar method of managing their data.
- IBM is adopting HDP for its Hadoop distribution and will fully integrate it with Data Science Experience and Machine Learning. As a result, this solution will combine for users the rich data security, governance and operations functionality provided by HDP, and the advanced analytics and management of the Data Science Experience. IBM will migrate existing IBM BigInsights users to HDP.
- IBM Data Science Experience provides a set of critical tools and a collaborative environment through which analysts and developers can create new analytic models quickly and easily. For example, IBM Machine Learning, found in the Data Science Experience, can speed the time it takes to build and deploy analytic models for application development by two-times, according to IBM testing.
- In addition to the companies’ numerous product collaborations, IBM and Hortonworks are also founding members of the Open Data Platform Initiative (ODPi). Launched in February 2015, ODPi is comprised of industry leaders working collaboratively to define and promote a set of standard open source technologies and increase compatibility among big data platforms.
- The expanded partnership also builds on an existing partnership and joint solutions including HortonWorks Data Platform (HDP) for IBM’s Power Systems and Spectrum Scale Storage. Customers can benefit from fast access to data and a cost-effective platform for running their big data and cognitive workloads.
- Earlier this week Hortonworks announced Hortonworks DataFlow (HDFTM) for IBM Power Systems. HDF, the industry’s only data ingest, stream processing and streaming analytics platform built entirely on open source software, is designed to enable customers to collect, curate, analyze and act on all data in real-time, across the data center and cloud. Combined with IBM Power Systems, customers can gain access to industry-leading performance and efficiency for streaming analytics. HDF is complementary to HDP and is designed to accelerate the flow of data in motion into HDP to support full fidelity analytics.
