Many companies used to talk about speeding up development and delivery of digital services. Then COVID-19 disrupted traditional ways of doing business, and the talking turned action. But much of digital transformation involves notoriously time-consuming data processes, and now there’s more pressure than ever to speed them up. 

Pain points around agility and time to value are top of mind for customers lately, according to Tom Deane (pictured, left), senior director of product management at Cloudera Inc. In many companies, a large number of staffers — data scientists, application developers, engineering and operations teams, for example — are needed to execute a single machine-learning or artificial-intelligence project. These teams badly need quicker provisioning and more streamlined processes. 

Deane and Abhinav Joshi (pictured, right), senior product marketing manager for OpenShift at Red Hat Inc., spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during the KubeCon + CloudNativeCon NA event. They discussed best practices for speeding up data projects and Cloudera and Red Hat’s collaboration and recent announcements. (* Disclosure below.)

Containers spin up a three-minute data warehouse

MLOps — the application of DevOps practices to machine learning — is evolving in places like Kubeflow, a cloud-native community focused on easily deploying and managing an ML stack on Kubernetes. In fact, the use of containers (a virtualized method for running distributed applications) and Kubernetes is key to more efficient data-driven app development, according to Deane.

Containers provide the rapidly provisioned infrastructure needed work with data for real-world business demands. Combining them with a platform designed to cover the end-to-end data lifecycle gives companies a way to put plans into action in an agile manner, according to Joshi. Red Hat found the data analytics and AI capabilities that would pair well with its OpenShift container platform in the Cloudera Data Platform.

“That was a key motivation to partner with Cloudera in terms of bringing this joint solution to market,” he said.

Joining its public cloud version, Cloudera Data Platform Private Cloud — announced a couple of months ago — brings cloud native practices inside on-premises data centers, according to Deane. Consider a single important early step in setting up a business for data science and ML/AI: the provisioning of a data warehouse. In the past, this might have taken three months; with containers, CDP Private Cloud customers can provision a data warehouse in three minutes, Deane added.

Also on CDP is the Shared Data Experience (SDX) suite of tools. It offers one global data set that a whole organization can access with shared security and governance. SDX may help address certain cultural and systemic problems — too many cooks in the kitchen, disparate sources of truth, access issues — that have long stymied data projects.

“By having these shared data experiences, our developers, our users can build these multidisciplinary workflows in a very straightforward way without having to create all this custom code that can stitch them together,” Deane stated. 

Companies must always choose data technologies with reference to business outcomes — and speeding up time to value is a big part of that, according to Joshi.

“At the end of the day, what all the customers are looking at doing is being able to improve the experience of their customer with the digital services they roll out … and gain a competitive advantage,” he concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the KubeCon + CloudNativeCon NA event. (* Disclosure: Red Hat Inc. sponsored this segment of theCUBE. Neither Red Hat nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE