When implementing data refresh strategies in Power BI for bigger models, it is always very important to improve on them in such a way that there's a good compromise between the age of the data and the performance of the system. Here are some ways in which you can configure incremental refresh more efficiently:
Partitioning Strategy: Determine parts of your data that are volatile and those that are fixed over time. For example, in the case of sales data, recent daily sales information may require fast updates, while older or historical sales data may not. Create partitions relevant to the time frame of the data to be refreshed, such that if the last few months or weeks of data are to be refreshed, then only that data is refreshed.
Use of Range Start And Range End Parameters: When defining the correctness of incremental refresh, besides the mentioned conditions, you will have to create two parameters – "RangeStart" and "RangeEnd." These two parameters represent the dates that have to be refreshed. These parameters will ensure that the data that is queried and refreshed is only that which falls within the specified range, hence eliminating unnecessary loads.
Polish the Data Queries: You can manipulate the Power Query itself so that the data gets loaded in Power BI only after applying the necessary filters. Ensure that the queries are as simple as possible and only reach the records needed for the incremental refresh period. This may require ignoring some loaded data or any other data that is not necessary, especially with large databases.
Evaluate and Track Efficiency: First, monitor the refresh process to determine whether it is performing within the input parameters set. Then, evaluate the length of time per refresh period and alter the settings accordingly. For instance, shrinking the data range or leaving some tables out of refresh may help shorten the refresh duration.
Define and Implement Retention Policies: Also, consider that, on the contrary, especially when within a given period, the target audience is given too many updates, and chances are performance might be affected. Retention policies should be employed to indicate the allowable period for keeping the respective dataset containing the historical information and, once surpassed, disposing of or storing the irrelevant data. This helps in the context that only the relevant data is kept in the model with Power BI on the users' behalf.
Concentrate on Power BI Premium Advantages: However, if you are on Power BI Premium, use the increment refresh and archive mechanism more effectively because of the additional resources and increased database size. Premium features, increased storage, and reduced refresh times can lessen the turnaround time for processing large tables.
Doing so will enhance the efficiency of data refresh without altering the integrity of the data published in your reports. Nonetheless, it should be noted that configuring incremental refresh policies greatly impacts the system's efficiency regarding data processing and currency.
