> ## Documentation Index
> Fetch the complete documentation index at: https://docs.domino.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Manage Spark dependencies

In a shared Spark cluster, it can be challenging for teams to manage their dependencies (for example, Python packages or JARs). Installing every dependency that a Spark application may need before it runs and dealing with version conflicts can be complex and time-consuming.

Domino allows you to easily package and manage dependencies as part of your [Spark-enabled compute environments](/cloud/platform-capabilities/features/compute-clusters-hpc/spark/on-demand-spark/configure-prerequisites#creating_base_spark_env). This approach creates the flexibility to manage dependencies for individual projects or workloads without having to deal with the complexity of a shared cluster.

To add a new dependency, add the appropriate statements in the *Docker Instructions* section of the relevant Spark and execution compute environments.

For example to add `numpy`, include the following.

```dockerfile theme={null}
### Optionally specify version if desired
RUN pip install numpy
```

## Next steps

[Access data with Spark](/cloud/platform-capabilities/features/compute-clusters-hpc/spark/on-demand-spark/work-with-data).