> For the complete documentation index, see [llms.txt](https://docs.valohai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.valohai.com/reusable-step-libraries/ecosystem-libraries.md).

# Using Valohai Ecosystem Libraries

Valohai maintains a collection of pre-built library steps for common workflows. These are production-ready and available to all organizations.

## Available libraries

### Database Connectors

Run SQL queries and save results to your data store:

* [BigQuery Connector](/reusable-step-libraries/ecosystem-libraries/bigquery.md)
* [AWS Redshift Connector](/reusable-step-libraries/ecosystem-libraries/redshift.md)
* [Snowflake Connector](/reusable-step-libraries/ecosystem-libraries/snowflake.md)

All database connectors:

* Accept SQL queries as parameters
* Save results as CSV outputs
* Support both credential-based and machine identity authentication
* Automatically version query results in your data store

### Docker Image Builder

Build and push Docker images without installing Docker locally:

* [Docker Image Builder](/reusable-step-libraries/build-your-own-library/docker-image-builder.md)

Supports AWS ECR, GCP Artifact Registry, and Docker Hub.

## How to use ecosystem libraries

Ecosystem libraries are automatically available in your organization. No setup required.

### Run a library step

1. Open your project
2. Click **Create Execution** under the Executions tab
3. Expand the step library by clicking the **+** next to **valohai-ecosystem** in the left panel
4. Select a library step (e.g., `bigquery-query`)
5. Configure parameters and environment variables
6. Click **Create Execution**

Library steps run like any other execution—same logs, same outputs, same metadata tracking.

## Example: Query BigQuery

Let's run a BigQuery query and save the results:

### Add environment variables

Under your project Settings or as an organization-wide environment variable group:

* `GCP_PROJECT` — Your GCP project ID
* `GCP_IAM` — Set to `1` to use machine identity, or `0` for keyfile auth
* `GCP_KEYFILE_CONTENTS_JSON` — (If using keyfile) Service account JSON

### Create the execution

1. Select the `bigquery-query` step from **valohai-ecosystem**
2. Write your SQL query in the **query** parameter:

```sql
SELECT user_id, COUNT(*) as events
FROM `my-project.analytics.events`
WHERE date >= '2025-01-01'
GROUP BY user_id
ORDER BY events DESC
LIMIT 100
```

3. (Optional) Set an output path like `top_users.csv`
4. (Optional) Add a datum alias like `latest-user-stats` for easy reference
5. Click **Create Execution**

The query runs on BigQuery, and results are saved to your data store. Use the output in other executions with `datum://latest-user-stats`.

## Why use ecosystem libraries?

**No setup needed:** No YAML to write, no Git repository to manage. Just run.

**Battle-tested:** These steps are maintained by Valohai and used across hundreds of organizations.

**Versioned results:** Query outputs are automatically tracked and versioned in your data store.

**Consistent patterns:** All connectors work the same way—write a query, get a CSV. Easy to learn once and reuse everywhere.

## Next steps

**Database connectors:**

* [BigQuery](/reusable-step-libraries/ecosystem-libraries/bigquery.md)
* [AWS Redshift](/reusable-step-libraries/ecosystem-libraries/redshift.md)
* [Snowflake](/reusable-step-libraries/ecosystem-libraries/snowflake.md)

**Build custom images:**

* [Docker Image Builder](/reusable-step-libraries/build-your-own-library/docker-image-builder.md)

**Create your own:**

* [Build Your Own Library](/reusable-step-libraries/build-your-own-library.md)


---

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