QSDA2024 Exam Dumps : Qlik Sense Data Architect Certification Exam – 2024

The scenario describes an app that is experiencing long load times due to the need to pull all records, both new and historical, from three large databases. This situation puts a strain on both the Qlik environment and the source databases. Given that all data is required for analysis, a full reload each time can be inefficient and resource-intensive.

Implementingincremental loadis a widely recommended approach in such cases. Incremental loading allows you to load only new or changed data since the last reload, rather than reloading all the data every time. This significantly reduces the time and resources required for reloading, as only a subset of the data needs to be processed during each reload. QVD (QlikView Data) files are typically used to store the historical data, while only the new or updated records are fetched from the source databases.

This approach would help:

Reduce the load on the source databases.

Shorten the data reload window.

Maintain historical data efficiently while ensuring that all new data is captured.

The scenario describes a need to track social media data over the past 10 years, capturing all changes (inserts, updates, deletes) while reloading the data four times a day.

To manage this:

ModificationTime: This field is essential for tracking changes over time. It indicates when a record was last modified, allowing the script to determine whether it needs to insert, update, or delete records.

Primary Key Field: A primary key is crucial for uniquely identifying records. It enables the script to match records in the source with those already loaded, facilitating updates and deletions.

Insert and Update Records: The script should handle both inserting new records and updating existing ones based on the ModificationTime.

Remove Records: If records are deleted in the source, they should also be removed in the Qlik Sense data model to maintain consistency.

This approach ensures that all changes in the social media data are accurately captured and reflected in the Qlik Sense application.

In Qlik Sense, when a data load script is executed and an error occurs, the script execution is halted immediately, and any tables that were being loaded at the time of the error are discarded. However, the existing data model—i.e., the last successfully loaded data model—remains intact and is not affected by the failed script. This ensures that the application retains the last known good state of the data, avoiding any partial or inconsistent data loads that could occur due to an error.

When the script encounters an error:

The tables that were successfully loaded prior to the error are retained in the session, but these tables are not merged with the existing data model.

The existing data model before the script was executed remains unchanged and is maintained.

No partial or incomplete data is loaded into the application; hence, the data model remains consistent and reliable.

Qlik Sense Data Architect ReferencesThis behavior is designed to protect the integrity of the data model. In scenarios where script execution fails, the user can debug and fix the script without risking the data integrity of the existing application. The key references include:

Qlik Help Documentation: Provides detailed information on how Qlik Sense handles script errors, highlighting that the existing data model remains unchanged after an error.

Data Load Editor Practices: Best practices dictate ensuring that the script is fully functional before executing it to avoid data inconsistency. In cases where an error occurs, understanding that the current data model is maintained helps in strategic debugging and script correction.