Import QVD to ODBC Automatically
Want to import QlikView QVD files into an ODBC-compliant database? Whether you're working with legacy systems or proprietary tools, Advanced ETL Processor makes the process reliable - no SQL, scripting, or custom coding required.
What Is a QVD File?
A QVD (QlikView Data) file is a proprietary format used by QlikView and Qlik Sense to store structured data efficiently. QVDs are optimized for in-memory analytics but require an intermediary tool to load into external databases via ODBC.
What Is ODBC?
ODBC (Open Database Connectivity) is a standardized API that allows applications to read and write data from a wide variety of database systems using a common interface. With an ODBC driver, you can connect to Oracle, SQL Server, DB2, Informix, MySQL, Access, and many others.
Why Import QVD via ODBC?
- Support virtually any database system with a compatible driver
- Centralize QlikView data in your existing enterprise infrastructure
- Enable analytics, dashboards, or application-level access to QVD content
How to Import QVD to an ODBC Database - Step-by-Step
1. Launch Advanced ETL Processor
Start the Enterprise Edition of Advanced ETL Processor. Open Tools > Connections and make sure both your QVD and ODBC connections are properly configured.
2. Create a New Transformation
Right-click on a transformation group and select New to create a new dataflow transformation.
3. Update Reader Properties
- Drag the QVD Reader onto the canvas
- Double-click to open the Properties dialog
- Select the correct QVD connection
- Choose the QVD file to import
4. Update Writer Properties
- Drag the ODBC Writer onto the canvas
- Double-click to open the Properties dialog
- Select the appropriate ODBC data source (DSN) and credentials
- Select the target table
5. Map and Transform Data
Use the AutoMap function or manually connect fields between the QVD Reader and ODBC Writer. You can apply data transformations such as formatting, type casting, or conditional logic.
6. Run the Import
Click Execute to begin the import process. The QVD data will be loaded into your ODBC-connected database. Save the transformation for reuse or automation.
7. Automate and Monitor
- Use the built-in scheduler to automate the job
- Trigger on file arrival, time intervals, or events
- Log every run and enable error handling and rollback if needed
Where this import fits
Import QVD to ODBC is useful when QVD data needs to feed ODBC, 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 ODBC so reporting teams can query historical Qlik data outside the dashboard layer.
BI migration staging
Move QVD data into ODBC staging tables before validation, reconciliation, and warehouse loading.
Scheduled analytics feed
Import recurring QVD files into ODBC with logs and rejected-row handling instead of manual export steps.
Video Tutorial
FAQ
Can Advanced ETL Processor import QVD to ODBC?
Yes. Advanced ETL Processor can read QVD, map fields, validate data, write to ODBC, 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 ODBC 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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