Getting Started With Dremio Data Reflections

For analytical workloads, data teams today have various options to choose from in terms of data warehouses and lakehouse query engines. To enable self-service, they provide a semantic layer for end users, usually with materialized views, BI extracts, or OLAP cubes. The problem is, this process creates data copies and requires end users to understand the underlying physical data model. 

Join the Dremio engineering team in this episode of Gnarly Data Waves to learn about accelerating your queries with data reflections. Get answers to business questions faster without the challenges that come with today’s approach, such as governing data copies or managing complex aggregate tables and materialized views.

In this episode, you will learn:

1. 1. The importance of data reflections and how it removes the need for data copies

1. 1. When to use raw reflections and aggregate reflections

1. 1. Best practices on data reflection refreshes