JupyterHub best practices for users

The JupyterHub best practices page describes several efficient ways to use JupyterHub to maintain a reproducible and performant workflow.

Use small and modular cells

Separate imports, transformations, visualizations, and outputs. This helps with the following:

To ensure your Spark jobs run reliably and make optimal use of cluster resources, follow these caching best practices:

Cache only when necessary using, cache or count:
```
df.cache()
df.count()
```
Clear the cache once finished.
```
spark.catalog.clearCache()
```
Avoid caching large DataFrames unless repeatedly reused.

Use Markdown cells to annotate notebooks using the following:

Readable notebooks improve handoffs and review cycles.

For long-running ETL or large datasets, adhere to the following:

For example:

spark-submit --master local[2] my_job.py

To keep the code reusable, adhere to the following:

To get an overview of JupyterHub, refer to the JupyterHub in NexusOne page.
To learn practical ways to use JupyterHub in the NexusOne environment, refer to the JupyterHub hands-on examples page.
For more details about JupyterHub, refer to the JupyterHub official documentation.