As you correctly mentioned, such libraries or connectors are typically used to interact with Qlik Sense APIs for operations like fetching data, not for pushing data to Qlik Sense.
To automate the process of transferring data from a PySpark DataFrame to a Qlik Sense environment, you would generally follow these steps:
-
Data Export: Export the PySpark DataFrame to a format that is compatible with Qlik Sense. This could be CSV, Excel, or other file formats that Qlik Sense can read.
-
File Transfer: Move the exported file to a location accessible by Qlik Sense. If you're using Qlik Sense Enterprise on Windows, this could be a network share. For Qlik Sense SaaS, you may need to upload the file to a cloud storage service that Qlik Sense can connect to, like Amazon S3 or Google Cloud Storage.
-
Qlik Sense Data Load: Use Qlik Sense data load scripts to import the data from the files into Qlik Sense applications.
Unfortunately, there is no direct method to convert a DataFrame to a QVD file in Python, as the QVD format is proprietary to Qlik. QVD files are typically generated by Qlik Sense or QlikView as part of the data load and transformation process.
For your production automation job, you would need to:
- Export the DataFrame to a CSV or another supported file format using PySpark's DataFrame writer.
- Transfer the file to a location that your Qlik Sense environment can access.
- Create a Qlik Sense data load script that reads from this file and loads the data into Qlik Sense, which can be triggered after the file transfer is complete.
Here's a basic example of how you might export a DataFrame to CSV in PySpark:
python
final_df.write.csv(path='path_to_export_directory', header=True, mode='overwrite')
And here's a simple example of a Qlik Sense load script that would read from a CSV file:
qlik
LOAD
column1,
column2,
column3
FROM [path_to_csv_file]
(txt, codepage is 1252, embedded labels, delimiter is ',', msq);
This load script would be part of a Qlik Sense app and would run within the Qlik Sense environment, not from an external Python script.
If you're looking for a more direct integration between a Spark cluster and Qlik Sense without using intermediate file storage, you would typically look into utilizing APIs or connectors, but this would require a read-compatible interface on the Spark cluster side.
