Cloudera Inc. said today it’s adding more “experiences” to its Cloudera Data Platform to make it easier for customers to extract more business value from their data.

The Cloudera Data Platform is positioned as a one-stop-shop cloud service for organizations that want to perform analytics across hybrid and multicloud environments with enterprise-grade security and governance.

The platform combines a cloud-native data warehouse, machine learning service and data hub, each running as instances within the self-contained application operating environments called containers. Queries are managed by Apache Hive or Apache Impala, the latter of which was developed by Cloudera.

Cloudera Data Platform provides real-time data processing and predictive analytics capabilities that enable a wide range of business applications. But the company is looking to expand its usefulness with today’s updates, which enhance the platform in three significant ways.

First up is Cloudera Data Engineering for CDP, which is meant to make it easier for customers to prepare their data across the entire lifecycle, so it can be analyzed with specific business use cases in mind.

Santiago Giraldo, senior product marketing manager of Data Engineering and Machine Learning at Cloudera, told SiliconANGLE that CDP Data Engineering makes it easier for data engineers to manage all of their data engineering workflows in one place. They can also use it to work directly with machine learning, data science or analytics teams to more quickly and securely deliver pipelines for specific workloads or projects.

“Let’s say a data science team is tasked with delivering data to six different machine learning teams and five different data warehousing teams across different regions and lines of business,” Giraldo said.

He explained that CDP Data Engineering enables data scientists to use centralized data from information technology departments without transferring or siloing that information first. They can then create individual virtual clusters for each project or team to use. In turn, that enables the data scientists to clean and curate the data exactly how each individual project team needs, without worrying about impacting other workloads or project teams, he said.

“Once they’ve built and automated delivery of these pipelines across all 11 teams, they can now continue to manage and monitor their jobs and pipelines from a single pane of glass, as well as set alerts,” Giraldo added. “This eliminates costly problems associated with the massively siloed approach many organizations use today.”

Also new in CDP is Cloudera Visual Applications for CDP Analytical Experiences, which gives analysts and data scientists an easy way to share and explain the results of their data analysis by creating rich, visual dashboards that business decision makers can easily understand.

Finally, Cloudera Operational Database is a new feature that will enable developers to build more data-driven business applications that can take advantage of the insights generated by CDP. Essentially, it’s a high-performance NoSQL database for cloud, on-premises and hybrid deployments that offers extremely high performance and scale for business applications that need to leverage real-time data.

“CDP Operational Database makes it dramatically simpler and easier to build new applications,” said Sushant Rao, a senior product marketing manager at Cloudera. “From an operational standpoint, it means developers are no longer responsible for the care and feeding of the cluster. And they no longer have to go digging into configurations to determine connection URLs, libraries needed and security configurations to get started.”

Cloudera said CDP Data Engineering is generally available on Amazon Web Services from today, while CDP Data Visualization and CDP Operational Database are currently available as a technical preview, with general availability expected later in the year.

Image: Cloudera