Dremio Innovates SQL Query Acceleration with Latest Reflections Technology

Dremio, the easy and open data lakehouse, is debuting its next-generation Reflections technology, aiming to revolutionize the landscape of SQL query acceleration.

According to the company, Dremio Reflections pave the way for sub-second analytics performance across an organization's entire data ecosystem, regardless of where the data resides. 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.

Reflections are Dremio’s innovative SQL query acceleration technology. Queries using Reflections often run 10 to 100 times faster than unaccelerated queries, according to the company.

The new launch introduces Dremio Reflection Recommender, a ground-breaking capability that lets users accelerate Business Intelligence workloads in seconds, according to the company. Reflection Recommender automatically evaluates an organization's SQL queries and generates a recommended Reflection to accelerate them.

Reflection Recommender eliminates arduous manual data and workload analysis, ensuring the fastest, most intelligent queries are effortless and only a few keystrokes away. Reflection Recommender is easy to use and puts advanced query acceleration technology into the hands of all users, saving time and cost.

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 approach eliminates the need for full data refreshes, resulting in faster updates and lower compute costs, according to the company.

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.

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