Implementing an incremental load strategy is the standard and most efficient solution for this scenario. This technique involves performing an initial full data load and storing it in an optimized Qlik Data (QVD) file. Subsequent reloads only fetch new or updated records from the source databases, which is a much smaller dataset. This new data is then combined with the historical data loaded from the fast-reading QVD file. This approach drastically reduces the data volume transferred from the source systems, significantly shortens the application reload time, and lessens the strain on the database servers, directly addressing all issues described in the problem.

A. Running reload tasks serially instead of in parallel would likely increase the total load time and does not solve the core problem of repeatedly pulling large volumes of historical data.

B. Direct Discovery is used for querying massive datasets directly at the source, not for optimizing the reload of data that is required in-memory for analysis. It doesn't solve the reload window issue.

D. On-demand app generation (ODAG) is a user-facing feature for analyzing subsets of big data on-the-fly. It is not a back-end data loading strategy for an app that requires all data.

1. Qlik Sense for Windows Help. (2024). Loading new and updated records with incremental load. Qlik Inc. This document explicitly details the process of using QVD files to load only new or changed records from a database, which is the solution for the described problem. It states, "Incremental load is useful for loading transaction data... By using incremental load, you can reduce the load on database systems and the network traffic."

2. Qlik Sense for Windows Help. (2024). Best practices for data modeling: QVD files. Qlik Inc. This section highlights the performance benefits of QVDs, stating, "A QVD file is read 10-100 times faster than reading from other data sources... This is a considerable performance enhancement when loading large data sets." This supports using QVDs for the historical data portion of an incremental load.

3. Qlik Sense for Windows Help. (2024). On-demand app generation. Qlik Inc. This source describes ODAG as a method to "load and analyze subsets of very large data stores," which confirms it is not the correct solution for a scenario where all data is required in the primary app.