Dremio Delivers GenAI-Powered Data Discovery and Unified Path to Apache Iceberg on the Data Lakehouse 

November 30, 2023

Easy and Open Data Lakehouse Technology Expands Generative AI Capabilities and Doubles Down on Simplification of Iceberg Adoption

LAS VEGAS – November 30, 2023 At AWS re:Invent, Dremio, the easy and open data lakehouse, today announced 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 best 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. Dremio’s GenAI capabilities make Dremio the easiest analytics platform for all data users, allowing faster and more accurate data insights.

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 best-in-class 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.”

To see these performance improvements in action visit this blog; and to download Dremio Self-Managed Software or access Dremio Cloud for free, visit www.dremio.com

About Dremio

Dremio is the easy and open data lakehouse, providing self-service analytics with data warehouse functionality and data lake flexibility across all of your data. Use Dremio's lightning-fast SQL query service and any other processing engine on the same data. Dremio increases agility with a revolutionary data-as-code approach that enables Git-like data experimentation, version control, and governance. In addition, Dremio eliminates data silos by enabling queries across data lakes, databases, and data warehouses, and by simplifying ingestion into the lakehouse. Dremio's fully managed service helps organizations get started with analytics in minutes, and automatically optimizes data for every workload. As the original creator of Apache Arrow and committed to Arrow and Iceberg’s community-driven standards, Dremio is on a mission to reinvent SQL for data lakes and meet customers where they are on their lakehouse journey.

Hundreds of global enterprises like JPMorgan Chase, Microsoft, Regeneron, and Allianz Global Investors use Dremio to deliver self-service analytics on the data lakehouse. Founded in 2015, Dremio is headquartered in Santa Clara. CNBC recognized Dremio as a Top Startup for the Enterprise and Deloitte named Dremio to its 2022 Technology Fast 500. To learn more, follow the company on GitHub, LinkedIn, Twitter, and Facebook, or visit www.dremio.com.

Media Contact

Elise Woodard

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