Highlight:

  • The updated platform from Denodo looks to assist organizations through enhanced features that connect and query disparate sources of data across different environments.

Denodo, a data virtualization vendor, is expanding its platform with an enhanced version that offers cloud connectivity and a data catalog. It is further benefited from machine learning (ML).

With the Denodo Platform 8.0, the company, based in Palo Alto, California, develops advanced data virtualization to build a logical data fabric framework. Out of new features, GraphQL allows organizations to access and connect various data sources across a data fabric. There are also advanced multi-cloud and hybrid integration and a modern web interface that looks to simplify management and ongoing operations.

With data virtualization, data resides at virtual pointers, instead of transferring data into a data lake or centralized repository. It’s an approach that David Stodder, Senior Director, The Data Warehousing Institute (TDWI) Research, Business Intelligence, sees solving some major challenges that organizations may face with data management.

Stodder added, “Organizations have a pain point when they have limited data access and have to go through significant data movement and centralization to provide access.” He also said, “A data virtualization layer can provide an alternative way of providing governed and authenticated access and querying of data at the sources rather than moving it to a central spot.”

Denodo Platform 8.0

Denodo aims to expand its data virtualization platform to offer a unified data delivery layer as a logical data fabric. It is also trying to integrate diverse components in a physically distributed environment, such as a multi-cloud and hybrid scenario. Platform 8.0 is ahead in offering platform-as-a-service (PaaS) support, thus managing the cloud infrastructure from a centralized web console.

As per Stodder, the novel data catalog capabilities in Denodo Platform 8.0 are a significant addition. TDWI prepares analytics, business intelligence (BI), and data science projects, thus seeing data cataloging and metadata management as top features for organizations that want to enhance users’ ability to explore, locate, and relate data. Stodder said, “The 8.0 release also employs AI for query performance and to enable AI-powered recommendations, which will help users be more productive and experience fewer delays.”

Alberto Pan, Executive VP & CTO at Denodo, commented: “The new data catalog includes personalized recommendations of potentially interesting data sets based on the user’s activity patterns and others like them.” Further, he added, “Recommendation techniques are also used in Denodo’s new smart query acceleration engine to automatically learn from past workloads and recommend summaries that can optimize workloads.”

Advantages of GraphQL support

GraphQL is one of the key additions in Denodo 8.0 that offers an alternative approach to connect APIs (application program interface) and data interface to the commonly deployed REST API. As per Pan, when using a REST API, applications need to call various endpoints to obtain all the data they require to perform a single action. Furthermore, with GraphQL, the application should obtain all that it needs with a single call to the API.

Pan commented: “This comes with some trade-offs, so it’s not that REST APIs are going away any time soon, but GraphQL APIs are definitively more convenient in some scenarios — and that is why the adoption of the technology is growing very fast.” Further, he added, “Generally speaking, creating GraphQL APIs requires significant coding work, which Denodo Platform 8.0 addresses for users.” Pan also said any virtual model created in Denodo is accessible using GraphQL out of the box.

ML in next Denodo update

Pan is planning to use ML in future updates for more points in the Denodo Platform. He said, “Denodo has a privileged position in its customers’ information architecture, as the bridge between data sources and data consumers. As such, Denodo can collect a lot of interesting information about access patterns, data sources, and performance.”

“This data can be used to feed machine learning processes to automate tasks at all the stages of the data management process,” Pan commented. “We have started doing this with the recommendations in the data catalog and for query acceleration, but we plan to extend it to many other areas.”