In my experience, partitioning tables is an effective technique for managing large datasets in Power BI. By breaking down the data into smaller partitions based on specific criteria like date ranges or regions, we can improve query performance by only accessing the relevant partitions. Additionally, using incremental data loading techniques, such as appending only new records or updating modified records, can help minimize the data refresh time and improve overall efficiency.

One technique is to optimize data loading and transformation using Power Query Editor, such as filtering unnecessary columns, removing duplicate records, and promoting proper data types. Additionally, utilizing DirectQuery or Live Connection can help to retrieve data directly from the data source without loading it into Power BI's memory. Another approach is using aggregations to summarize and store pre-aggregated data to enhance query performance.

I found that leveraging composite models in Power BI Desktop is a great way to handle large datasets. By combining DirectQuery, Import, and/or Composite models, we can optimize the performance of reports and dashboards while still maintaining the interactive capabilities of Power BI. This technique allows us to use DirectQuery for large tables while importing smaller tables to improve the user experience.

To handle large datasets, leveraging Power BI dataflows can be quite beneficial. With dataflows, you can extract, transform, and load data from various sources into a Common Data Model (CDM). This approach provides a centralized and scalable solution for managing large and complex datasets. By utilizing Power Query Online and incremental refresh, you can significantly reduce data refresh times and improve the overall performance of reports and dashboards.