Dremio Reflections

What is Dremio Reflections?

Dremio Reflections is a functionality provided by Dremio that focuses on minimizing data processing times and improving analytical capabilities. It builds on the principles of Materialized Views, creating optimized representations of the original data to speed up read operations. This ability to accelerate queries and reduce computational workload is a game-changer for businesses dealing with large and complex datasets.

Functionality and Features

Dremio Reflections works by creating Reflections, which are optimized subsets or aggregates of the original data. The Dremio query optimizer automatically leverages these Reflections to accelerate query performances. Key features include:

  • Automatic selection of Reflections based on user queries
  • Advanced indexing and partitioning for efficient data reading
  • Support for raw and aggregated data Reflections
  • Incremental updates to keep Reflections in sync with source data
  • Storage optimization through data compression and columnar formatting


Dremio Reflections is an integral part of Dremio's architecture, which is built on Apache Arrow, a columnar memory model that enhances performance. The Reflections work in tandem with Dremio's query optimizer to facilitate faster and more efficient data processing.

Benefits and Use Cases

Businesses can leverage Dremio Reflections in many ways. For instance, it improves the performance of BI tools, supports faster analytical processes, and reduces the load on source systems. Other benefits include:

  • Enabling rapid data exploration and analysis
  • Reducing costs associated with scaling up infrastructure
  • Minimizing dependencies on ETL processes
  • Simplifying data architecture by eliminating the need for cubing, indexing, or aggregating data

Integration with Data Lakehouse

In a data lakehouse environment, Dremio Reflections plays a crucial role in accelerating data processing and analytic performance. Its ability to create Reflections from raw data stored in the data lake provides a seamless bridge between the versatility of a data lake and the structured convenience of a data warehouse - the two components of a lakehouse.

Security Aspects

Dremio Reflections adopts the security features of Dremio, including a fine-grained access control system and encryption. It ensures that Reflections follow the same access and security rules as the source data.


Through Reflections, Dremio significantly reduces query times, optimizing performance. It also supports the use of less computational power, resulting in cost savings.


How does Dremio Reflections improve query performance? Dremio Reflections enhances query performance by creating optimized subsets and aggregates of the original data which the Dremio query optimizer can leverage to expedite data processing.

How does Dremio Reflections interact with a data lakehouse environment? In a data lakehouse setup, Dremio Reflections can create accelerated query paths from the raw data stored in the data lake, bridging the gap between the flexibility of a data lake and the structured efficiency of a data warehouse.

What security measures does Dremio Reflections adopt? Dremio Reflections inherits Dremio's security measures, including fine-grained access control and encryption. Reflections follow the same security protocols as the source data.


Reflections: In Dremio, Reflections refer to optimized subsets or aggregates of source data created to speed up queries.

Query Optimizer: A component of Dremio that automatically chooses the best Reflections to speed up data processing.

Data Lakehouse: A hybrid data management platform that combines the benefits of data lakes and data warehouses.

Apache Arrow: An open-source, columnar memory computing project used in Dremio to enhance performance.

ETL: Extract, Transform, Load - a method of copying data from one or more sources into a destination system which represents the data differently from the source or in a different context.

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