Explanation:
The most efficient and secure method to make the data_utils library available to all PySpark jobs on a Databricks cluster is by adding it to the cluster's library dependencies through spark.conf settings. This approach ensures the library is accessible across all notebooks and jobs without the need for individual installations or environment variable modifications. Other methods, such as using %pip install, setting PYTHONPATH, or switching to the Databricks Runtime for Data Engineering, either do not provide cluster-wide availability or are unnecessarily complex.
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How can a data engineering team efficiently make the Python library data_utils available to PySpark jobs across multiple notebooks on a Databricks cluster?
A
Run %pip install data_utils once on any notebook attached to the cluster.
B
Edit the cluster to use the Databricks Runtime for Data Engineering.
C
Set the PYTHONPATH variable in the cluster configuration to include the path to data_utils.
D
Add data_utils to the cluster's library dependencies using the spark.conf settings.
E
There is no way to make the data_utils library available to PySpark jobs on a cluster.
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