Hi @RoastedPork ,
You can optimize your data model using following tips:
Remove unused tables or columns, where possible.
Avoid distinct counts on fields with high cardinality – that is, millions of distinct values.
Take steps to avoid fields with unnecessary precision and high cardinality. For example, you could split highly unique datetime values into separate columns – for example, month, year, date, and so on. Or, where possible, use rounding on high-precision fields to lower cardinality – (for example, 13.29889 -> 13.3).
Use integers instead of strings, where possible.
Be wary of DAX functions, which need to test every row in a table – for example, RANKX – in the worst case, these functions can exponentially increase run-time and memory requirements given linear increases in table size.
When connecting to data sources via DirectQuery, consider indexing columns that are commonly filtered or sliced again. Indexing greatly improves report responsiveness.
Enable Row-Level Security (RLS) where applicable.
Use Microsoft AppSource certified custom visuals where applicable.
Do not use hierarchical filters.
Provide data categorization for Power BI reports (HBI, MBI, LBI).
Use the On-premises data gateway instead of Personal Gateway.
Use slicers sparingly.
To minimize the impact of network latency, strive to keep data sources, gateways, and your Power BI cluster as close as possible. If network latency is an issue, try locating gateways and data sources closer to your Power BI cluster by placing them on virtual machines.
To further improve network latency, consider using Azure ExpressRoute, which is able of creating faster, more reliable network connections between your clients and Azure datacenters.
For more details you could take a look at this official document.
https://docs.microsoft.com/en-us/power-bi/transform-model/dataflows/dataflows-understand-optimize-re...
Best Regards,
Jay
