Dremio, the easy and open data lakehouse, has launched its next-generation Reflections technology, revolutionizing the landscape of SQL query acceleration. Dremio Reflections pave the way for sub-second analytics performance across an organization’s entire data ecosystem, regardless of where the data resides. According to the company, this transformative technology redefines data access and analysis, ensuring that insights can be derived swiftly and efficiently and at 1/3 the cost of a cloud data warehouse.

“Dremio Reflections accelerate SQL queries by orders of magnitude, eliminating the need for BI extracts/imports and enabling companies to run their most mission-critical BI workloads directly on a lakehouse,” said Tomer Shiran, founder of Dremio. “With automatic recommendations and next-generation incremental updates, we’ve made it even easier for organizations to take advantage of this innovative technology.”

Queries using Reflections often run 10 to 100 times faster than unaccelerated queries. The new launch introduces Dremio Reflection Recommender, a capability that lets you accelerate Business Intelligence workloads in seconds. Reflection Recommender automatically evaluates an organization’s SQL queries and generates a recommended Reflection to accelerate them.

Dremio has also optimized how Reflections are refreshed to further enhance query performance and drive cost efficiencies. Dremio now intelligently refreshes Reflections on Apache Iceberg tables to instantly capture incremental data changes. This powerful approach eliminates the need for full data refreshes, resulting in faster updates and lower compute costs.

Dremio Reflections eliminate performance challenges for BI dashboards and reports, and eliminate the need for data teams to export data from the lakehouse into BI extracts or imports for analytics. With Reflections, there’s no need to create precomputed tables in the data lake or data warehouse to achieve the sub-second performance for BI workloads, reducing work and complexity for data teams.