From news of Databricks Inc. acquiring Tabular Technologies Inc. and its independent storage engine to Snowflake Inc. open-sourcing its Polaris Catalog, the data management space has been buzzing with significant activity.

It’s no coincidence that this flurry is happening to support the boom in enterprise artificial intelligence, automation and advanced analytics. How will the resulting status quo position long-time players, such as Cloudera Inc., on the strategy and value delivery fronts?

“The first thing I would observe is that this is a validation of our strategy,” said Venkat Rajaji (pictured, right), senior vice president of product management at Cloudera. “We moved into the Iceberg way about two years ago. We’re evolving, and where we’ve been moving over the last number of years is we provide the foundation for building and deploying AI applications, including chatbots, document summarization and code generation. Last week, we announced the acquisition of Verta’s Operational AI platform.”

Rajaji and Sanjeev Mohan (middle), principal at SanjMo, spoke with theCUBE analyst Rob Strechay (left), during a CUBE Conversation at SiliconANGLE Media’s livestreaming studio in Boston. They discussed the implications of recent data management announcements as they signal a shift toward open standards and interoperability. (* Disclosure below.)

Data management: Metadata, interoperability and Cloudera’s strategic positioning

Cloudera mapped the foreshocks before the data earthquake. By embracing Apache Iceberg, it is future proofed with a high-performance format for large-scale data sets. Now, with over 25 exabytes of data under management, Cloudera offers a hybrid open data lakehouse that bridges analytics and AI. Acquiring Verta also bolsters that long-term AI value proposition, according to Rajaji.

“Our decision to make this strategic acquisition isn’t just about growth,” he said. “It’s about delivering an enriched AI experience to our customers and anticipating their needs since the future of data management is AI. Data is the foundation for AI and AI applications and where the market is headed.”

Given the proliferation of tools for different usage scenarios in the modern data stack, interoperability has become crucial to simplify data management across formats and platforms. Embracing open standards gives end-user companies the value of streamlined, more efficient workflows, according to Mohan.

“Expecting clients to take their data and move it into wherever AI is running is not going to happen,” he said. “It’s not practical because of the cost, the time and the security risk of moving this data. What I’m starting to see is that the pendulum has shifted once again. They want the ability to bring any compute engine that they may have, any processing engine. This is the reason why Iceberg has now risen to be one of the hottest topics.”

The pendulum has shifted, and metadata has become key to the interoperability paradigm by bridging across different data processing engines and enhancing the overall reliability of AI models. Metadata provides the context needed for accurate insight generation from data, Mohan added.

“Whoever has the control over the metadata has the keys to the kingdom,” he said. “Metadata is used not just for interoperability of our reports, dashboards and traditional analytics, but we also rely on metadata to help improve accuracy and reduce hallucinations of our LLMs. Metadata is providing the context of all this massive wealth of data that we have collected over the past few decades.”

Here’s theCUBE’s complete video interview with Venkat Rajaji and Sanjeev Mohan: