# Computed Table vs Computed File

Source: https://userguide.qualytics.io/container/computed-tables/deep-dive/computed-table-vs-computed-file/

You reached this page from the Computed Tables sub-tree. If you already know you want a warehouse-backed derived table, keep reading; if the raw data lives in files on object storage, jump to [Computed File vs Computed Table](https://userguide.qualytics.io/container/computed-files/deep-dive/computed-file-vs-computed-table/) instead.

## When a Computed Table fits

A Computed Table is the right choice when:

- The source data lives in a **JDBC warehouse** (Snowflake, PostgreSQL, Redshift, SQL Server, Oracle, BigQuery, Databricks, and similar), or in a **native datastore** (AWS Glue Native, Hive Native, MongoDB, Unity Catalog Native).
- On a JDBC warehouse, you want to reuse the **warehouse's own SQL dialect** and its query planner (index-aware, statistics-aware, cost-based), with the query running inside the warehouse itself. On a native datastore the query is Spark SQL and runs in Qualytics.
- The transformation needs **joins across multiple base tables or views** in the same datastore.
- You do not need Qualytics to profile the source container before you build the Computed Table.

## When a Computed File fits instead

Switch to a [Computed File](https://userguide.qualytics.io/container/computed-files/deep-dive/introduction/) if:

- The source data lives in **files on object storage** (Amazon S3, Google Cloud Storage, Azure Data Lake Storage).
- The files are Parquet, ORC, Avro, CSV, TSV, PSV, JSON, Excel, Delta, or Iceberg.
- You want to normalize columns, cast types, or flatten nested arrays (via lateral views + `EXPLODE`) using Spark SQL.
- You are OK with a single-source restriction (one `source_container_id` per Computed File, no joins inside the container itself).

## Side-by-side comparison

| Feature | Computed Table (JDBC or native) | Computed File (DFS) |
| --- | --- | --- |
| Source datastore | JDBC warehouse or native datastore | DFS (S3, GCS, Azure Data Lake Storage) |
| Query language | Warehouse's own SQL dialect on JDBC, Spark SQL on native | Spark SQL |
| Number of source containers | Any base tables and views in the datastore | One `source_container_id` |
| Joins inside the container | Yes, across base tables of the same datastore | No, use a [Computed Join](https://userguide.qualytics.io/container/computed-join/getting-started/) |
| Source pre-profile required | No | Yes, at least once |
| `DISTINCT` in the projection | Allowed if the warehouse allows it | Not allowed, use `GROUP BY` |
| Fully-qualified name requirement | SQL Server, Oracle, Redshift | Not applicable |
| Execution runtime | Warehouse engine on JDBC, Qualytics's Dataplane on native | Qualytics's Dataplane |
| Materialized on storage | No (metadata-only definition run on demand) | No (metadata-only definition run on demand) |

*Last updated: November 9, 2026.*