How do you optimize data models for performance when working with very large files in Power BI?
Use the metadata below to identify which tables are the biggest refresh bottlenecks. Return the largest tables by estimated model size, along with their row counts, column counts, and last refresh time. Ignore rows where estimated_size_mb is NULL.
table_name, row_count, column_count, estimated_size_mb, last_refresh_atestimated_size_mbestimated_size_mb descending, then row_count descending, then table_name ascending| Column | Type | Description |
|---|---|---|
| table_idPK | INT | Primary key for the table metadata |
| table_name | VARCHAR(100) | Power BI table name |
| row_count | INT | Estimated number of rows in the table |
| estimated_size_mb | NUMERIC(10,2) | Estimated model size in megabytes |
| last_refresh_at | TIMESTAMP | Timestamp of the last refresh |
| Column | Type | Description |
|---|---|---|
| column_idPK | INT | Primary key for the column metadata |
| table_id | INT | References powerbi_tables.table_id |
| column_name | VARCHAR(100) | Column name in the Power BI table |
| data_type | VARCHAR(50) | Column data type |
| is_hidden | BOOLEAN | Whether the column is hidden in the model |