> For the complete documentation index, see [llms.txt](https://docs.syntho.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.syntho.ai/setup-workspaces/create-a-workspace/connect-to-a-database/databricks/importing-data-into-databricks.md).

# Importing Data into Databricks

Once your synthetic data is written as Parquet files to a storage location (Local Filesystem, Azure Data Lake Storage (ADLS), or Amazon S3), follow these steps to import it back into Databricks:

1. **Access Databricks workspace**: Go to your Databricks workspace and navigate to the Data tab.<br>
2. **Select your data source**:
   * For ADLS, select Azure Data Lake.
   * For Amazon S3, choose Amazon S3.
   * If using the Local Filesystem, upload your files to a cloud storage service like ADLS or S3 first.<br>
3. **Mount the storage**: Mount your cloud storage (ADLS or S3) to Databricks following the [Databricks mounting documentation](https://docs.databricks.com/data/data-sources/azure/azure-datalake-gen2.html) for ADLS or [S3](https://docs.databricks.com/data/data-sources/aws/amazon-s3.html).<br>
4. **Read the parquet files**: Use the Databricks Data tab or a notebook to load Parquet files into a DataFrame. For details, check the [Databricks guide on reading files](https://docs.databricks.com/data/data-sources/read-parquet.html).<br>
5. **Create or register a table**: Use Databricks SQL commands or the user interface to create a temporary or permanent table from the loaded data. Parquet files generated by Syntho can be loaded into Databricks using standard Databricks SQL commands. For example:

```sql
CREATE TABLE example_table
USING PARQUET
LOCATION 'dbfs:/FileStore/tables/example.parquet';
```

This command creates a table from the specified Parquet file. Refer to the [Databricks documentation](https://docs.databricks.com/data/tables.html) for more information on managing tables.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.syntho.ai/setup-workspaces/create-a-workspace/connect-to-a-database/databricks/importing-data-into-databricks.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.