June 27, 2023
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:
- The importance of data reflections and how it removes the need for data copies
- When to use raw reflections and aggregate reflections
- Best practices on data reflection refreshes
Watch or listen on your favorite platform
Register to view episode
Ready to Get Started? Here Are Some Resources to Help
What Is a Data Lakehouse?
The data lakehouse is a new architecture that combines the best parts of data lakes and data warehouses. Learn more about the data lakehouse and its key advantages.read more
Simplifying Data Mesh for Self-Service Analytics on an Open Data Lakehouse
The adoption of data mesh as a decentralized data management approach has become popular in recent years, helping teams overcome challenges associated with centralized data architecture.read more