A comparative study between Materialized Views and Dremio Data Reflections, highlighting their applications and benefits.

Materialized views and Dremio data reflections are both techniques for pre-computing data queries to improve the performance of subsequent queries. However, there are some critical differences between the two techniques.
Materialized views are a traditional database concept, and they are typically implemented as tables populated with the results of pre-computed queries.

Dremio data reflections on the other hand, are Apache Iceberg representations of a view of the raw data or aggregations that can apply custom sorting, partitioning, and other optimizations.

We will discuss the challenges of Materialized views and how Data Reflections meet these challenges with a more flexible and robust approach unique to the Dremio Data Lakehouse platform.

This presentation will also explore the different types of Reflections:

- Raw reflections: These reflections consist of all of the rows and one or more fields of the underlying table or view that they are created from. They can be customized by vertically partitioning data (choosing a subset of fields), horizontally partitioning the data (by defining one or more columns to be partition keys), and sorting the data on one or more fields.

- Aggregation reflections: These reflections accelerate BI-style queries that involve aggregations (GROUP BY queries). They can also be configured to work on a subset of the fields of a data source.

Benefits of using Dremio data reflections:

- Improved performance: Dremio data reflections can significantly improve the performance of queries by pre-computing the results of those queries.

- Flexibility: Dremio data reflections can be dynamically generated based on the results of user queries, making them more flexible than traditional materialized views.

- Scalability: Dremio data reflections can be scaled to handle large datasets.