Dremio Sonar is a lakehouse query engine that supports the full spectrum of SQL needed for an organization’s data consumers.
- Business users and analysts benefit from Sonar’s query acceleration and semantic layer to power BI dashboards directly on the lakehouse, without stale and expensive data extracts.
- Data engineers can use the intuitive UI to quickly provision new views and metrics without ETL work.
- Data scientists benefit from Sonar’s native Arrow Flight interface, enabling high-throughput data access from data science tools and programming languages like Python.
Sonar also breaks down data silos and enables queries on data that is not only in a lakehouse, but in databases and data warehouses, across Amazon Web Services (AWS), and on-premises.
A data source can be a data lake, such as Amazon S3 and AWS Glue Catalog, or a relational database (referred to as an external source). For more information, see Connecting to Your Data.
A space is a directory in which views are saved. Spaces allow people in your organization to group datasets by common themes, such as purposes, departments, or geographic regions. You can also create folders within spaces to organize your datasets further. When you join a Sonar project, your user ID is given its own home space by default. For more information, see Spaces.
Tables and Views
A table contains the data from your source, formatted as rows and columns. A table cannot be modified by Sonar.
A view is a virtual table, created by running SQL statements or functions on a table or another view.
To learn more, see Datasets.
A reflection is an optimized materialization of source data or a query, similar to a materialized view, that is derived from an existing table or view. To learn more, see Accelerating Queries with Reflections.