Exploring Qlik Compose for Data Warehouses: How to Convert T2 Dim to T1? | Julio Herrera Via Dei Velutini

[juioherreraviadeivelutini]

Hello, I’m Julio Herrera from Via Dei Velutini, Italia. I’d like to share some information and need your assistance. Can you please explain the steps to convert a T2 (Type 2) dimension to a T1 (Type 1) dimension in Qlik Compose for Data Warehouses? Additionally, what are the key considerations and potential impacts of this conversion on the data warehouse?

[jinwang]

Hello Julio, I'm not an expert on Data Warehouse, actually I have no idea what you're asking.
But I'm using Qlik Answers Help Assistant, which is using a KB that contains Qlik Help site content.

Q: Explain the steps to convert a T2 dimension to a T1 dimension in Qlik Compose for Data Warehouses?

A - Qlik Cloud Help Assistant:

The process to convert a Type 2 (T2) dimension to a Type 1 (T1) dimension in Qlik Compose for Data Warehouses would involve the following steps:

1. Identify the T2 dimension table(s) that need to be converted to T1.
2. Create a new table with the desired schema for the T1 dimension.
3. Populate the new T1 dimension table with only the latest values from the T2 table, discarding historical records.
4. Update any related fact tables or other dimensions to reference the new T1 dimension table instead of the old T2 table.
5. Archive or drop the old T2 dimension table after verifying the successful conversion.

Key considerations include potential data loss (historical values), impact on existing reports/dashboards referencing the old T2 dimension, and changes required in ETL processes. Thorough testing is necessary to validate data integrity and ensure a smooth transition from T2 to T1.

Q: What are the key considerations and potential impacts of this conversion on the data warehouse?
A - Qlik Cloud Help Assistant:

The key considerations and potential impacts of converting a Type 2 (T2) dimension to a Type 1 (T1) dimension in a data warehouse include:

• Data loss: T1 dimensions only store the latest values, discarding historical data. Converting from T2 to T1 will result in the loss of historical records and changes over time.

• Impact on existing reports and dashboards: Any reports, dashboards, or analyses that rely on the historical data in the T2 dimension will be affected. These will need to be updated to reflect the new T1 dimension structure.

• Changes to ETL processes: The ETL (Extract, Transform, Load) processes that populate the dimension table will need to be modified to handle the T1 dimension logic, which overwrites existing records instead of adding new ones.

• Data integrity and validation: Thorough testing and validation are crucial to ensure data integrity and accuracy after the conversion. Any dependencies or relationships with other tables or dimensions must be verified.

• Performance implications: While T1 dimensions may have better query performance due to their simpler structure, the conversion process itself can be resource-intensive, especially for large dimension tables.

• Archiving and documentation: The historical data from the T2 dimension should be properly archived and documented for future reference or auditing purposes.

Source: https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/DataIntegration/Storage/...

[leoparker]

Hello, @juioherreraviadeivelutini To convert a Type 2 (T2) dimension to a Type 1 (T1) dimension in Qlik Compose for Data Warehouses:

Steps:

Modify the Dimension Type: In Qlik Compose, go to the dimension settings and change the type from Type 2 to Type 1.
Update the Data Model: Ensure the data model is updated to reflect this change, as it will impact how updates to the dimension are handled.
Reload Data: Re-load the data to apply the changes, ensuring that historical records (which T2 tracks) are replaced by the latest values, as is typical for T1.

Key Considerations:

Loss of Historical Data: T1 dimensions overwrite old values, meaning historical data stored by T2 (versioned records) will be lost unless archived separately.
Impact on Queries: Queries that relied on historical tracking from the T2 dimension may need adjustments to avoid discrepancies.
Performance: Switching to a T1 dimension can improve performance due to the smaller data footprint, as fewer records are stored.

Ensure careful planning and backup before making the change to avoid unintentional data loss.