Import QVD to Posgresql Automatically
Importing QlikView QVD files into PostgreSQL doesn’t need to involve manual scripts or custom ETL pipelines. With Advanced ETL Processor, you can automate the entire process - no SQL, Python, or programming required.
What Is a QVD File?
A QVD (QlikView Data) file is a proprietary file format used by QlikView and Qlik Sense to store structured data in a compressed, high-performance format. While great for analytics within Qlik, it is not directly compatible with PostgreSQL - unless you use an ETL tool that supports it.
What Is PostgreSQL?
PostgreSQL is a powerful open-source relational database known for its reliability, extensibility, and support for advanced SQL features. It’s commonly used in enterprise, web, and analytical applications.
Why Import QVD into PostgreSQL?
- Enable SQL-based reporting and analysis on Qlik data
- Combine QVD content with other PostgreSQL data sources
- Make QlikView data available to web apps, APIs, and BI platforms
How to Import QVD to PostgreSQL - Step-by-Step
1. Launch Advanced ETL Processor
Start the Enterprise Edition of Advanced ETL Processor. Go to Tools > Connections and configure connections for both QVD files and your PostgreSQL database.
2. Create a New Transformation
Right-click on a transformation group and select New. This opens the dataflow canvas.
3. Update Reader Properties
- Drag the QVD Reader onto the canvas (if not already placed)
- Double-click to open the Properties dialog
- Select the appropriate QVD connection
- Select the QVD file you want to import
4. Update Writer Properties
- Drag the PostgreSQL Writer onto the canvas (if not already placed)
- Double-click to open the Properties dialog
- Select the appropriate PostgreSQL connection
- Select the target schema and table
5. Map and Transform Data
Connect fields between the QVD Reader and PostgreSQL Writer. Use the AutoMap feature or manually map fields. Apply transformations such as trimming, type conversion, and formatting directly within the interface.
6. Run the Import
Click Execute to run the transformation. The QVD data will be imported into your PostgreSQL database. Save the transformation for reuse or automation.
7. Automate and Monitor
- Use the built-in scheduler to run the job on a schedule
- Trigger jobs based on file availability or system events
- Enable logging, error alerts, and rollback capabilities
Where this import fits
Import QVD to Posgresql is useful when QVD data needs to feed Posgresql, reporting databases, operational systems, migration jobs, or downstream ETL workflows. Use it when the same import needs validation, mapping, transformations, scheduling, and logs instead of another manual load.
Do not automate the import until the source layout, target table, key fields, write mode, and failure behaviour are agreed. Automation repeats rules; it does not rescue unclear ones.
Useful links for this import
Start with the Advanced ETL Processor Enterprise overview, then download the fully functional 30-day trial. The import-data hub lists the other QVD import workflows.
Business usage examples
Qlik reporting archive
Load QVD extracts into PostgreSQL so reporting teams can query historical Qlik data outside the dashboard layer.
BI migration staging
Move QVD data into PostgreSQL staging tables before validation, reconciliation, and warehouse loading.
Scheduled analytics feed
Import recurring QVD files into PostgreSQL with logs and rejected-row handling instead of manual export steps.
Video Tutorial
FAQ
Can Advanced ETL Processor import QVD to Posgresql?
Yes. Advanced ETL Processor can read QVD, map fields, validate data, write to Posgresql, and log the import.
Do I need to write scripts for the import?
No scripting is required for the normal import workflow. You can configure the reader, writer, mapping, validation, and schedule visually.
Can the QVD import run on a schedule?
Yes. The package can run on a schedule, process matching QVD files, archive originals, and write rows to Posgresql with the same validation rules each time.
Can imported data be transformed before loading?
Yes. You can clean values, convert data types, calculate fields, split columns, and apply lookup rules before writing to the target.
Can bad rows be logged or rejected?
Yes. Add validation rules so rejected rows, failed files, row counts, and error details are visible after each run.
What should I check before the first production import?
Check source layout, target table, key fields, data types, date formats, write mode, archive folder, and failure handling.
When should I not automate the import yet?
Do not automate it until the source layout, target table, key fields, and bad-row handling are clear. Automation repeats rules; it does not invent them.
Can I test the import before buying?
Yes. Download the fully functional 30-day trial, build one small import, and test it with a deliberately awkward sample file.
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