Here are some tips to help maintain performance and efficiency in scaling Power Pivot data models as the size of the dataset grows:
Optimize Data Types and Formatting: Use the most efficient data types for columns, such as integers for IDs instead of text, and avoid unnecessary precision in numerical fields. Proper formatting minimizes memory usage and improves query performance.
Use Measures Over Calculated Columns: Storing the calculated columns in memory makes the model bigger, while measures are computed at runtime. Replacing all calculated columns with measures wherever possible will save memory and enhance scalability.
Reduce Model Complexity: To make the model leaner, remove unneeded columns and tables. Avoid complex relationships or too many tables with slow performance. Summarization vs. Granularity: If a higher level is the only required granularity, consider summarizing the data at that level.
Relationship Optimization and Cardinalities: Foot-and-mouth relationships are always one-way, not whipping up these heavy resource guzzlers that are many-to-many. Once again, low cardinality (i.e., lower unique values) in columns involved in relationships significantly boosts performance.
Aggregate Data at the Source: Any operation related to data summarization and uploading it to the Power Pivot source will reduce the amount of data that gets into the model.
Support Compression: Power Pivot compresses data as a matter of course. Properly charge columns and disembowel them to enhance the effectiveness and efficiency of compression.
Partitioning of Large Dataihsis: This breaks large datasets into smaller parts, loading only the required datasets for analysis. This makes a very effective memory handling.
