13 Jun 2017 San Jose - IBM and Hortonworks have expanded 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", stated 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", stated 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:
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, 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 centre 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.
As part of their wide-ranging partnership, the companies will also team to advance the development of Unified Governance - IBM BigIntegrate, IBM BigQuality and IBM Information Governance Catalogue - on the Apache Atlas open platform. Atlas provides a scalable governance platform for Enterprise Hadoop which is designed to help developers model new business processes and data assets quickly and easily. Through their work, both companies plan to help advance Atlas from its current Incubator status to Apache Top Level Project status, where projects are typically released for open development and deployment.
In addition to Atlas, the companies will also partner on the advancement of Apache Spark, the open source framework for processing and analyzing large data sets across clustered environments. The companies will also collaborate to advance the Apache Hadoop framework itself, working to unify access to multi-vendor, heterogeneous data environments across data warehouses and databases - ultimately aiming to simplify the environment for better value from all data.