IBM today announced a broad expansion of its Cloud Data Services portfolio, with more than 25 services now available on the IBM Cloud.
The new IBM cloud services are designed to help developers build, deploy and manage Web and mobile applications and enable data scientists to discover hidden trends using data and analytics in the cloud.
“We recognize that the world of data science is a very attractive profession for a lot of people and there are a lot of aspiring people out there who want to get more familiar with the tools of the trade,” Adam Kocoloski, IBM Distinguished Engineer and CTO of Cloud Data Services, told eWEEK, explaining why IBM is making more services available to even entry-level data users.
The new hybrid cloud services can be deployed across multiple cloud providers and are based on open-source technologies, open ecosystems that include company and third-party data, and open architectures that allow data to easily flow amongst the different services.
In addition to self-service capabilities for everything from data preparation, migration and integration to tools for advanced data exploration and modeling, IBM introduced four key new services: IBM Compose Enterprise, IBM Graph, IBM Predictive Analytics and IBM Analytics Exchange.
IBM Compose Enterprise is a managed platform designed to help development teams build modern Web-scale apps faster by enabling them to deploy business-ready open-source databases in minutes on their own dedicated cloud servers.
“It gives a development team the freedom of choice that they’re looking for in this modern, polyglot world, while offering the IT organization some level of regularization and standardization around how those database management systems are deployed and how they’re scaled,” he said. “What this enterprise-class offering does is allow people to take advantage of that cloud native, multi-tenant environment in a world where they bring their own hardware and provision the platform for their own organization.”
IBM Graph is a managed graph database service built on Apache TinkerPop that provides developers with a complete stack to extend business-ready apps with real-time recommendations, fraud detection, Internet of things (IoT) and network analysis uses. TinkerPop is an open-source graph computing framework.
“The graph offering is a fully managed graph database with a TinkerPop3 interface and the Gremlin query language,” Kocoloski said. “IBM played a big role in bringing TinkerPop to Apache and building a community around that as a standard interface for graph databases. And with this service we now have a way to put the capabilities of a graph datastore into the hands of more users without asking them to go and stand up their own stack be it Cassandra, Titan, OrientDB or Neo4j. They don’t have to learn an unfamiliar database and learn its quirks to be able to start experimenting with graph algorithms and graph traversals as part of their applications. There is a broad class of problems that are amenable to the application of a graph technology, and we think that providing a managed cloud service is going to encourage more adoption of that in applications.”
IBM said IBM Graph delivers the only enterprise-grade graph database as a service, built on Apache TinkerPop, the leading open-source graph technology stack. Provided as a service, IBM Graph helps remove the complexities traditionally associated with moving data from existing databases to graph architectures, IBM said.
“It is good to see Apache TinkerPop and the Gremlin graph traversal language being adopted as the primary interface to IBM’s Graph service,” said Marko Rodriguez, Apache TinkerPop Project Management Committee member, in a statement. “IBM was instrumental in pushing TinkerPop to join the Apache Software Foundation which is important because Apache provides a commercial-friendly license and a tried-and-true open source development model that has done wonders for TinkerPop’s software and community. I hope other large enterprises follow IBM’s decision to leverage Apache TinkerPop in their respective graph products and services.”
IBM Ups Cloud Data Portfolio for Developers, Data Scientists
IBM Predictive Analytics is a service that enables developers to easily self-build machine learning models from a broad library into applications to help deliver predictions for specific product use cases, without the help of a data scientist.
“What we’re driving at here is a way to get more folks to incorporate machine learning into their analytic operations and to enable those models to be deployed into applications,” Kocoloski said. “So instead of asking someone to spin up a Hadoop cluster and then take the model they developed and figure out how to write an API to power it, we’re leveraging some of the capabilities we have in our SPSS portfolio and taking that and ensuring that models that are built in SPSS can be exposed as APIs to applications. Also, with this, users who may not even be familiar with SPSS can start doing model development on the cloud to do signal extraction and work with data sets in a new way.”
He added that the Predictive Analytics service is one way that people can start to get more familiar with what it means to use machine learning algorithms and cognitive algorithms in the increasingly noisy world of data. Under the hood, it takes advantage of an auto-modeling capability in IBM’s SPSS predictive analytics product.
“We’re trying to help people by proactively choosing an appropriate set of models that are most likely to be able to extract signals,” Kocoloski said. “The bar we’ve set for ourselves is that it’s not about the sheer scope of feature set and functionality here; it’s rather about how consumable can we make this offering. We identified a new class of user and we want to get them more familiar with this world.”
Finally, the IBM Analytics Exchange is an open data exchange that includes a catalog of more than 150 publicly available datasets that can be used for analysis or integrated into applications.
The analytics exchange plays into the overall “open for data” theme and the desire to make sure that public datasets are immediately accessible and able to be incorporated into a broader analytics routine, Kocoloski said.
“We think there is quite a lot of value to be gained from blending enterprise data with data from public and premium data sets,” he said. “But often one of the challenges there is just knowing what data sets are out there. So with the exchange we’ve built a comprehensive catalog of datasets, and it also sets the foundation for better cataloging and metadata management in the cloud native fashion. That’s an area that’s ripe for innovation.”
The new offerings build on IBM’s investment in Apache Spark and further complement its mission to provide enterprise-class support for open-source developers and data handlers of any level. IBM has redesigned more than 25 of the company’s core analytics and commerce solutions with Apache Spark—helping to dramatically accelerate their real-time processing capabilities.
“Data is the common thread within the enterprise, regardless of where its source might be. In the past, data handlers have relied on disparate systems for data needs, but our goal is to move data into the future by providing a one-stop shop to access, build, develop and explore data,” said Derek Schoettle, general manager of IBM’s Analytics Platform and Cloud Data Services, in a statement. “IBM’s integrated Cloud Data Services give developers greater scalability and flexibility to build, deploy and manage web and mobile cloud applications, and enable data scientists to apply information across businesses efficiently.”