# System requirements for Databricks

{% hint style="info" %}
If you want to use a version or provider that Structural does not support, contact Tonic.ai.
{% endhint %}

## Supported versions of Databricks

The following table lists the supported versions of Databricks, along with the corresponding version of Spark, based on [this information in the Databricks documentation](https://docs.databricks.com/aws/en/release-notes/runtime/).

| Databricks version | Spark version |
| ------------------ | ------------- |
| 14.3 LTS           | 3.5.0         |
| 13.3 LTS           | 3.4.1         |
| 12.2 LTS           | 3.3.2         |
| 11.3 LTS           | 3.3.0         |
| 10.4 LTS           | 3.2.1         |
| 9.1 LTS            | 3.1.2         |

## Supported providers

Structural supports the following data providers:

<table><thead><tr><th valign="top">Source Provider</th><th valign="top">Output Provider</th></tr></thead><tbody><tr><td valign="top">Parquet</td><td valign="top">Parquet</td></tr><tr><td valign="top">CSV</td><td valign="top">Parquet</td></tr><tr><td valign="top">Avro</td><td valign="top">Avro</td></tr><tr><td valign="top">JSON</td><td valign="top">JSON</td></tr><tr><td valign="top">ORC</td><td valign="top">ORC</td></tr><tr><td valign="top">Delta</td><td valign="top">Delta</td></tr></tbody></table>

## Supported table types

Databricks supports both MANAGED and EXTERNAL tables.

* MANAGED tables store all of their data within Databricks.
* EXTERNAL tables store their data on a separate file system (often Amazon S3).&#x20;

Structural can read from both table types. When it writes output data, Structural only writes to EXTERNAL tables.

## Must have clustering columns

Your Databricks tables must have clustering columns.