# SQL databases[¶](https://doc.dataiku.com/dss/latest/connecting/sql_datasets.html#sql-databases "Permalink to this headline")

DSS can both read and write datasets in SQL databases. You can:

* Create datasets representing SQL tables (and read and write in them)

* Create datasets representing the results of a SQL query (and read them)

* Write code recipes that create datasets using the results of a SQL query on existing SQL datasets. See SQL recipes for more information.

In addition, on most supported databases, DSS is able to:

* Execute Visual recipes directly in-database (ie: for a visual recipe from the database to the database, the data never moves out of the database)

* Execute Charts directly in-database

For more information on the range of what DSS can do with SQL databases, please see DSS and SQL.

Note

You might want to start with our detailed tutorial for your first steps with SQL databases in DSS.

You might want to start with that tutorial. The rest of this section is reference information.

DSS provides full support for many databases and experimental support for others. Click on a link for detailed support information for that database.

* Introduction

* Snowflake

* Azure Synapse

* Google BigQuery

* Amazon Redshift

* PostgreSQL

* MySQL

* Microsoft SQL Server

* Oracle

* Teradata

* Pivotal Greenplum

* Google AlloyDB

* AWS Athena

* Vertica

* SAP HANA

* IBM Netezza

* Exasol

* IBM DB2

* kdb+
