Understanding Incremental Load in QlikView

What Is Incremental Loading?

Incremental load is a technique that loads only new or changed data rather than the entire dataset. In QlikView, this is typically achieved using QVDs and time-stamped fields or change flags.

// Pseudocode example
LET vLastLoadDate = peek('LastUpdated', -1, 'qvd_table');

LOAD *
FROM source_table
WHERE LastUpdated > $(vLastLoadDate);

While efficient, this logic becomes fragile when:

• Source systems do not maintain reliable timestamps.
• Time zones or clock drift lead to missed updates.
• QVD corruption or missed reloads cause state desynchronization.

Architectural Implications

Dependencies on External Systems

Incremental loading in QlikView is often dependent on metadata from source systems like SQL Server, Oracle, or SAP. If those systems change schema, purge history, or truncate tables without updates to Qlik logic, loads break silently.

Reload Task Coupling

In QlikView Publisher or QMC (Qlik Management Console), task chains are tightly coupled. A failure in an upstream job may leave downstream apps reading from stale QVDs, causing data drift.

Diagnostic Strategy

Step 1: Analyze Load Logs

Start by parsing QlikView log files in the ReloadLogs folder. Focus on delta load statements, number of rows loaded, and timestamps.

LOAD *
FROM $(LogPath)\MyApp.log
WHERE Text LIKE '%rows fetched%';

Step 2: Validate QVD Freshness

Use QlikView's file functions to check the age of QVDs and compare with expected reload intervals.

LET vQvdAge = FileTime('MyFact.qvd');

Step 3: Audit LastUpdated Field Logic

Scan for inconsistencies in datetime formats, time zone mismatches, or nulls that bypass filters.

Step-by-Step Fixes

1. Harden the LastUpdated Field

Ensure that the LastUpdated field is consistent, indexed, and populated on every row from the source system.

2. Introduce Reload Validation Logic

After each incremental load, validate row counts and date ranges to detect anomalies early.

IF NoOfRows('FactTable') = 0 THEN
  TRACE 'WARNING: FactTable loaded zero rows';
ENDIF

3. Decouple Load Chains

Break monolithic task dependencies in QMC by introducing data quality flags, QVD state indicators, or independent triggers.

4. Add Fallback Full Loads Periodically

Configure monthly or weekly full reloads to mitigate drift and ensure synchronization.

5. Centralize Incremental Logic

Maintain reusable scripts in include files for all apps to promote consistency and reduce divergence.

Best Practices for Enterprise QlikView Deployments

• Use consistent date formats (e.g., ISO 8601) across all systems feeding QlikView.
• Introduce logging dashboards that monitor QVD freshness and row counts.
• Define a data contract with source teams for change tracking reliability.
• Leverage QlikView Governance Dashboard to audit reload performance and failures.
• Document all assumptions made by incremental logic in technical design specs.

Conclusion

Incremental loads are essential for scaling QlikView, but without careful planning and monitoring, they become a liability. Root causes such as schema drift, silent reload failures, and inconsistent metadata can lead to data loss or duplicate insights. For enterprise-scale platforms, investing in diagnostic tooling, reload validation, and architectural decoupling ensures that incremental logic is reliable and maintainable. QlikView's power lies not just in visualization but in the robustness of the pipeline that feeds it.

FAQs

1. How do I detect QVD staleness in QlikView?

Use the FileTime() function to compare QVD modification times against expected reload schedules.

2. Why are incremental loads missing some updates?

This usually results from inconsistent or missing LastUpdated values in the source, or incorrect timestamp filtering logic.

3. Can I simulate incremental logic during development?

Yes. Use variable overrides or mock QVDs to simulate prior loads and test different scenarios in sandbox environments.

4. How should I handle schema changes in source tables?

Maintain a schema registry or validation step before reloads. Use alerts or governance tools to flag unexpected changes.

5. Is it safe to mix full and incremental loads?

Yes, as long as metadata tracking is well-defined and duplication is prevented. Many enterprises schedule periodic full loads to reconcile data drift.