Question

How would you implement a FIFO data retention policy for a Power BI dataset?

I need guidance on implementing a First-In-First-Out (FIFO) data retention policy within a Power BI dataset. This policy is important to ensure that outdated data is automatically removed from the dataset as new data arrives, keeping the dataset within a specific size limit. What are the best practices or strategies for achieving this in Power BI?

Answer 1

To maintain the data refresh process for automated removal of outdated data for a dataset within size limits in Power BI, the strategy is implementing the FIFO (First-In-First-Out) data retention policy as follows:

Build a Timestamp Column: Add a timestamp column in your data source, which contains the time date each record was added or updated. This will help you determine the oldest data when deciding which records to delete.

Data Transformation in Power Query: Add a transformation step that filters out records from data older than a certain threshold from the timestamp. For instance, older than 30 days. You can use functions such as DateTime.LocalNow() and calculate the difference between the current date and the timestamp column to remove them through filtering.

Set Up Incremental Data Refresh: Incremental data refresh is supported in the huge dataset of Power BI Premium and Pro versions. The data refreshed using this method is the most recent, so the entire dataset will not be refreshed. You can set up the feature to fetch only the newly added records, deleting the older records automatically. Set the refresh policy so that older records will be deleted by a particular user-defined range so that Power BI would hold only the data needed for reporting.

Organize the Data: You may want to organize data into date-based divisions, such as months or weeks, to manage size and improve performance when dealing with a data set. With this partitioning strategy, Power BI processes only those portions with the new data, making the retention FIFO easier to manage.

Data Load Optimization and Removal:

1. If you have large datasets, try to load only some of them into Power BI at a time.
2. Wherever possible, prefer direct query models or incremental loads from the data source to keep a small window of data that never stops being refreshed.
3. For the remaining data stored in a database, use stored procedures or create scheduled jobs that deal with the deletion of older records by FIFO policy.

Automate Cleanup Tasks: With non-Power BI tools, such as SQL Server or other back-end systems, establish an automation routine to delete old records within a certain timeframe or for a fixed number of rows. Thus, Power BI will fetch and refresh only the necessary data.

These steps will allow you to set up FIFO data retention policies that keep your dataset within size limits in Power BI while keeping the newest records around for analysis.