Question

How can I optimize Power BI’s refresh schedule to avoid peak load times and ensure quick data availability?

I'm working on a Power BI project where data refreshes can sometimes overlap with peak load times, causing delays and slower performance. I want to optimize the refresh schedule to ensure data is updated efficiently and available quickly, especially for reports accessed frequently by business users. What are the best practices for scheduling data refreshes in Power BI to avoid peak times and maintain quick data availability?

Answer 1

Some tangible methods can be implemented to make Power BI's refresh schedule more efficient, especially during peak loads.

• Identify Off-Peak Hours for Scheduling: Examine the user engagement across specific Power BI reports to determine which of them has the lowest user activity. Generally speaking, schedule data refreshes for periods when this is the case, for example, at midnight or early in the morning when the users' activity is lowered to avoid congesting the servers during working hours. This will prevent unnecessary delays and also ensure that the information needed by users is updated in a timely manner.
• Stagger Refresh Times for Large Datasets: In the case where a workspace in Power BI consists of a number of large datasets, the schedule of refresh for these resources may be overlapped to ease the distribution of the load. Rather than refreshing every dataset at the same time, which can be costly in resources, create a refresh schedule that staggers the times (for example, 1 dataset at 1 AM, another at 2 AM, and so forth). This lessens the chances of resource overload and allows for better refreshing speeds for each dataset.
• Utilize Incremental Refresh: When dealing with large tables or datasets, one of the advantages of Power BI is the available incremental refresh. It saves both on refresh times and resource usage. Instead of refreshing an entire dataset, refreshing only augmentations in data or changes to the existing data is termed incremental refresh. This is particularly useful under circumstances where the reports are periodically refreshed, but there is no need to refresh the whole data set every single time.
• Improve Query Performance: A slow query, for example, could delay the refresh process, especially with complicated data sources. In Power Query, check your questions and make sure they are optimized. The refreshing process can be quicker if the data undergoes no or minimal transformation and is filtered at the origin.
• Use Dataflows When There Are Common Data Sources: On the other hand, if many reports are built across the same data sources, it might be worth looking into utilizing Power BI Dataflows to develop and provide these data transformations. The idea behind it is that the data source is refreshed once and used in several reports instead of refreshing each report individually.

In addition to these tips, you can also fine-tune the Power BI refresh schedule to avoid scheduling it during peak loads and ensure that the data is still available for the business users who need it in a timely manner. 

Answer 2

In the case of refresher schedules for Power BI:

Avoid Peak Hours: Do refreshes load-wise during off-peak hours.

Stagger Refreshes: If a number of datasets are used, schedule refresh times at some intervals to avoid overlap between datasets.

Use Incremental Refresh: For larger datasets, use incremental refresh to update only recently available records.

Monitor Refresh Times: View the refresh duration and readjust your timings based on performance insight.

Leverage DirectQuery: If data is needed in real-time, the direct query can be considered an alternative source for scheduled refreshes.

This practice ensures efficient data refreshing and availability.