Capacity Modeling: Power Pivot models that process transactional data to millions of rows should not slow their performance efficiency. This is possible only with data management efficiency, memory optimization, speed optimization, and the relevant tools for Power Pivot. Thus, even with large datasets, analysis accuracy has been maintained in terms of responsiveness and smoothness performance.
Data Reduction and Pre-Aggregation: Reduce the initial volume of the data before it hits Power Pivot with SQL queries or Power Query, which exclude rows and columns irrelevant to the result. Where possible, aggregate data-summarize transaction data on a monthly or category basis so that the model can compute smaller, more "significant" databases to avoid unnecessary memory use while maintaining insight.
Enhancing Relationships and DAX Design effective relationships in the data model. Where appropriate, use a star schema with transactional fact tables linked to dimension tables. Avoid complex many-to-many relationships, which can significantly slow down calculations. Further, optimize DAX measures using variables (VAR) for repeated calculations and avoid iterative functions like FILTER or SUMX when alternatives exist.
Also, Efficient Column Storage: Power Pivot is a columnar storage type that can gain the advantage of compressible data types. Free-form text columns should be replaced with numbers or codes (for example, by using category IDs instead of names) to improve compression and reduce memory usage. Eliminating unnecessary columns further reduces model size.
Largely Handle With Data through Azure
If the source data is put in Azure, you will be able to transmit data super fast. Tools such as Azure Synapse Analytics or Azure SQL Database for preprocessing and aggregating before bringing it into Power Pivot can do so, performing much of the heavy calculations or aggregations through the cloud at the infrastructure underpinned with both computing and storage by Azure, reducing the load on local machines. In such a manner, only necessary and optimized data is pulled into the model that improves performance.
