Maintaining semantic models that are large in Power BI will require you to leverage a range of optimization techniques, data model modeling best practices, and the knowledge of the platform. In the initial phase you should use star schema modeling to minimize the number of relationships and improve performance. You should also normalize the facts when needed, and eliminate any unintentional duplicate columns to limit unnecessary memory consumption.
Strategic selections around aggregations will also be useful - try to define summary tables that encompass metrics that will be queried frequently, and allow the Power BI service to automate switching from aggregate to detail data. Another optimization is to disable the auto date/time features to lessen model size and make full use of explicit date tables for when building time intelligence calculations.
As part of a Power BI Course in Pune, various professionals are learning to utilize great tools like Tabular Editor and DAX Studio to analyze and optimize semantic models at scale, and gain better performance and manageability on their sides.
You should also consider partitioning large tables, and if possible, enable incremental refresh, to ensure your refresh times are significantly reduced to offer better scalability. Also, remember to monitor the size of your dataset and document relationships accordingly.
Many participants in the Power BI Training in Pune are mentored through real life examples to build effective semantic layers. Likewise, participants in Power BI Classes in Pune often will follow labs that offer them practical advice on how to manage large semantic models whilst maintaining performance, governance, and usability across teams and projects.