Digital natives have led the way in pioneering big data open source production projects, but that doesn't mean that enterprises aren't interested in implementing and getting business value out of these technologies, too.
The need to move faster and be more agile is often one of the big drivers for traditional enterprises looking to implement these technologies.
[IBM is a big proponent of Apache Spark. To find out more read IBM, Databricks Advance Apache Spark.]
"What we've observed in conversations with customers is a strong desire to enable teams to move more quickly, to be agile while participating in the larger collaboration that happens to solve problems in large enterprises," Adam Kocoloski, IBM's CTO of Cloud Data Services and cofounder of the IBM acquisition Cloudant, told InformationWeek in an interview. "How do I have my team function like a startup, but in a more coordinated way?"
That's a challenging problem, and probably why big data vendors who offer distributions of Hadoop and other open source technologies are offering, many times, a whole stack of technologies that make big data infrastructure implementations a little easier to consume and digest.
To ease the way for its own customers, IBM has led with its BlueMix cloud platform, and additional data services and analytics under the new tag line, "Open for data."
The introduction marks an expansion of the company's Cloud Data Services with more than 25 services and 150 publicly available datasets available 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, IBM said in a Feb. 4 statement announcing the marketplace.
This new marketplace comprises several existing IBM services, including IBM Compose Enterprise, IBM Graph, IBM Predictive Analytics, and IBM Analytics Exchange.
IBM Compose Enterprise service brings private cloud support to a service previously only offered in the public cloud. The service grows out of IBM's acquisition of Compose last summer. It offers a managed platform to help development teams build Web-scale apps faster, making it easier to deploy open source databases such as MongoDB, Redis, Elasticsearch, PostgreSQL, and others, on companies' own dedicated cloud servers. Kocoloski told InformationWeek that the service offers enterprises flexibility and security, and opens up the cloud consumption model to more regulated workloads.
The company is also adding IBM Graph, which it describes as a fully managed graph database service built on Apache TinkerPop. It provides developers with a complete stack to extend business-ready apps with real-time recommendations, fraud detection, IoT, and network analysis uses, according to IBM.
Kocoloski said that Compose and Graph will help organizations with application development and IT collaboration, and they will complement the company's strategy around analytics. The services also speak to the "magnitude of our commitment to Apache Spark. We've been offering Spark as a fully managed cloud service for a while now." Now organizations can connect all these databases to Spark using Compose.
Another new service is IBM Predictive Analytics, which the company says will allow developers to easily build machine learning models from a broad library into applications to help deliver predictions for specific use-cases without the help of a data scientist.
"We want to let people dip their toe in the water of machine learning and predictive analytics with auto-modeling," Kocoloski said. "We think it's a smooth on ramp that is easier than pointing people to a book on Apache Hadoop."
Finally, IBM is also offering the IBM Analytics Exchange, the 150 publicly available datasets that can be used for analysis or integrated into applications.
The ultimate goal of offering all these services is to help companies get value out of big data technology.
"More than any individual data management system, it's the question of how can my teams move more quickly with autonomy," said Kocoloski. "Organizations may be set on a core repository but have no idea how to make data accessible in a self-service fashion to be able to deliver it with confidence to other parts of the business. This is the next phase of where the industry is headed," and the motivation behind the phrase "open for data."
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