The latest update from the data lakehouse specialist includes text-to-SQL translation capabilities and new partnerships that help create an ecosystem for GenAI development.
Capabilities that address speed, cost control, ease of use and an open environment for developing generative AI models are all part of the latest platform update from Dremio.
The vendor's new features, revealed on May 2 during a live user event in New York City, include expansion of its Apache Iceberg-backed data lakehouse to suit any deployment environment, generative AI tools including text-to-SQL translation and integrations that enable AI model development.
Taken together, the new features have the potential to substantially benefit Dremio customers, according to Stephen Catanzano, an analyst at TechTarget's Enterprise Strategy Group. In particular, the integrations will be beneficial given that Dremio's data lakehouse platform stores data, but needs to connect to other systems for analysis.
It's an important announcement to say they have interoperability with leading analytics companies and others in the GenAI space. The lakehouse is really a data repository, and they have some capabilities internally, but need to partner to enable GenAI and analytics to deliver solutions.Stephen CatanzanoAnalyst, Enterprise Strategy Group
"It's an important announcement to say they have interoperability with leading analytics companies and others in the GenAI space," Catanzano said. "The lakehouse is really a data repository, and they have some capabilities internally, but need to partner to enable GenAI and analytics to deliver solutions."
Based in Santa Clara, Calif., Dremio is a data lakehouse vendor whose tools combine the structured data management capabilities of data warehouses with the unstructured data management capabilities of data lakes. Because lakehouses enable users to combine disparate types of data to create large data sets that provide a complete view of an organization's operation, they are one of the preferred repositories for data that can be used to train AI models and applications, including generative AI.
Read the full story, via TechTarget.
