Dremio introduces GenAI-powered data documentation and labeling to reduce manual work
Dremio has unveiled AI-powered data discovery capabilities that accelerate and simplify data contextualization and description for analytics, along with improved capabilities that extend its leadership as the analytics engine for Apache Iceberg.
Expanding on previously announced Generative AI text-to-SQL capabilities, Dremio is delivering new GenAI-powered data documentation and labeling to provide comprehensive business context for analytics, while reducing manual work.
Mark Sear, director of data at Maersk, expressed his enthusiasm for these innovative additions to Dremio, stating, “Maersk is excited to embrace the transformative capabilities of Generative AI. Our data team is leveraging this cutting-edge technology to expedite and streamline data analytics for all users. This helps us more easily and precisely understand and analyze our data, delivering faster insights.”
Dremio is also extending its leadership as the analytics engine for Apache Iceberg, delivering a unified path to Iceberg for all data. Apache Iceberg is an open table format that is endorsed by tech giants such as Netflix and Apple.
With this release, Dremio makes it simple for companies to adopt Iceberg through one-click command ingestion into Iceberg tables. Dremio can seamlessly convert raw data (in JSON, CSV, and Parquet formats) from data lakes, relational databases, data warehouses, and NoSQL databases into Apache Iceberg, both in the cloud and on premises. This allows organizations to quickly and scalably migrate from less performant and functional formats to a modern, open table format that supports data warehouse functionality on the data lake, enabling a data lakehouse.
“Dremio is the fastest and most advanced query engine for Apache Iceberg,” said Tomer Shiran, founder of Dremio.
“These advances make it simple for companies to effortlessly adopt Iceberg and benefit from the table format’s performance, flexibility, and cost-savings while delivering data warehouse functionality on the data lake,” Shiran concluded.