# Using the Delta Sharing Capability in a Custom Script Environment

Here is an example of how to configure Delta Sharing capability with custom Python scripts:

1. Create shared data on Databricks.
2. After the shared data is configured, a Delta Sharing credential file can be downloaded.
See [Delta Sharing open sharing workflow](https://docs.databricks.com/en/delta-sharing/share-data-open.md#delta-sharing-open-sharing-workflow) for additional details.
3. Create a workflow based on the processing needs.
4. Execute a custom script (python code) from the workflow.
5. Register Delta Sharing credentials to workflow secrets.


Use the JSON content of your Delta Sharing credentials to supply a Secret Key.

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXfW_dJ8vUi17U05fth0OxpTNu-Ri9UTbsjRvtWe_V5ePM3tHlrv0q9CGCmiW1HFro7A3lbj8JJiM8KopapfdPbiksantTOgVtWdIcBf0VDbCiSJ_K5G1hVswBvsJHIuszEwZ13d8uueVKUGRfhJyWDHA9zIv482r5kYHyp6gw?key=odNQrvH9tO5QyI9DeUwa6g)

1. Define a workflow task to call a custom Python script.


In the following example, note that

* The Py> operator is used to call a specific method in your Python program.
* The Delta Sharing credentials are passed from the workflow secret to the container using the ${secret:configShare} environment variable.



```yaml
timezone: UTC
+call_exmple:
  docker:
    image: 'digdag/digdag-python:3.10'
  py>: delta_sharing_task.fetch_data
  _env:
    configShare: '${secret:configShare}'
```

In the Python program, PySpark is used to access shared data on Databricks, and the data can be manipulated as a standard spark dataframe.


```python
import sys  
import os  
import json  
from pyspark.sql import SparkSession  
  
  
def fetch_data():  
  # Install and import delta-sharing package  
  os.system(f'{sys.executable} -m pip install delta-sharing')  
  import delta_sharing  
    
    
  # Prepare config.share file  
  with open("config.share", "w") as outfile:  
    outfile.write(os.environ.get('configShare'))  
      
      
  # Fetch shared data on Databricks via Delta-Sharing server by PySpark  
  table_url = f"./config.share#your_share.your_database.your_table"  
  spark = SparkSession.builder \  
    .appName("delta-sharing-app") \  
    .master("local[*]") \  
    .config('spark.jars.packages', 'org.apache.hadoop:hadoop-azure:3.3.1,io.delta:delta-core_2.12:2.2.0,io.delta:delta-sharing-spark_2.12:0.6.2') \  
    .config('spark.sql.extensions', 'io.delta.sql.DeltaSparkSessionExtension') \  
    .config('spark.sql.catalog.spark_catalog', 'org.apache.spark.sql.delta.catalog.DeltaCatalog') \  
    .getOrCreate()  
  shared_df = spark.read.format("deltaSharing").load(table_url).select("column1", "column2", "column3").show()
```

This requires that the Delta Sharing library has been installed and imported.