Focusing on designing an appropriate model for the data, implementing a phased strategy of data loading, and using various load-suppressing techniques makes it possible to reduce the size of the Power BI file PBIX while handling large data sets. The following are some of the measures that can work toward this.
Data reduction and cleaning: There is every reason to examine the data that you are bringing in and see which, if any, columns and rows you can do without for your report. A feature selection analysis may reveal features (columns) that are only infrequently referenced in your target metrics. It is, after all, obvious that freeing the data set off redundant existing columns lowers the volume of data and, hence, the size of the file. In the case of big data, which undergoes constant changes, the “Believe that which is refresh” strategy that you spoke of is also helpful: deliberate only updating rows in foreign key fields that were changed in the source, and not re-importing bulk of the data.
Date Optimization: In an ordinary course, Power BI will automatically build a date table for any date column defined in the model, causing an unnecessary increase in file size. This feature may be turned off under the Power BI Options menu and improves the size of the file by significant margins of 50%-75% if many date columns exist, and the auto-generation is done for all. For such reasons, it is advisable to include a specific date dimension in the data model, especially for time-required insights, as it is more effective and gives one a better management of the time intervals and levels of data.
Effective Design of Data-Related Models: One of the best practices in data model design is the use of a star schema when laid out. The implementation of such a design goes a long way in reducing the size of the files by a great margin since it reduces the amount of redundancy. Also, make sure that all the relationships are defined appropriately, and turn off the Auto-Detect Relationships option when it causes joins that are unnecessary or incorrect. Also, limiting the cardinality of some of the columns, such as by rounding off numbers that do not require high accuracy or restrict the amount of descriptive text, plays a role in resource saving.
Data Compression: VertiPaq, the storage engine that comes with Power BI, performs well in data presentation but has room for improvement. This is because numeric data types such as integers are compressed better than the others. Low cardinality text columns are also discouraged because they need to be compressed better. In addition, with the exception of extreme cases where a very long description is necessary, consider shortening or excluding large text sections, especially if they only provide a descriptive context to the text.
Thus, you can also shrink pix files more easily by implementing characteristics such as data cleansing, proper data management, hierarchically structured databases, and well-deployed compression techniques in Power BI. This helps to deal with these performance issues by enhancing the management of large data sets. See the Power BI help section for more information on how to reduce the volume of data and file size further.
