Hi DeGuy,
Please be aware that, Azure Synapse Notebook allows you to pass only a single value out using the mssparkutils.notebook.exit()
function. If you want to pass an entire dataframe, there's no direct way to do this. However, you can work around it.
One common method is to save your dataframe to a file (like Parquet, CSV, or JSON) in a known location in your Azure Data Lake or Blob Storage, and then read that file in your Azure Data Factory (ADF) pipeline. Here is an example of how you can save a dataframe as a CSV file:
df.coalesce(1).write.format('com.databricks.spark.csv').option('header', 'true').save('abfss://your-data-lake@your-storage-account.dfs.core.windows.net/folder/file.csv')
In this example, df
is your dataframe, 'com.databricks.spark.csv'
is the format you want to write in (CSV in this case), and the last argument is the path where you want to save the file.
In your ADF pipeline, you can read the CSV file using a Copy Activity or another appropriate activity.
The key to this approach is that the notebook and the pipeline have a shared understanding of where the data is being written and read.
If you need to pass only a few fields or some aggregate information about your dataframe, you can create a dictionary or a JSON object, serialize it into a string, and pass that string using mssparkutils.notebook.exit()
.
# Let's assume df_summary contains some summary information about your dataframe.
df_summary = {"column1_avg": avg1, "column2_max": max2, "column3_min": min3}
summary_str = json.dumps(df_summary)
mssparkutils.notebook.exit(summary_str)
Then, in your ADF pipeline, you can use the @json()
function to parse the JSON string back into an object.
Remember, the approach to use depends on your specific needs and the size and complexity of your data.
I hope this helps?