Forum Discussion
Need Advice: Optimise Datasheet Storage Space (Reduce No. of Rows)
HI Angel_S_M ,
check below points:
Data Aggregation: Instead of storing each individual data point, you can aggregate the data at a higher level. For example, you can calculate the average, minimum, maximum, or any other relevant summary statistics for each frequency range or time period. This can significantly reduce the number of rows and still provide meaningful information for plotting the curve.
Data Compression: Power BI supports data compression techniques, which can help reduce the storage space and improve processing speed. Power BI automatically applies compression algorithms to minimize the size of your dataset. You can also manually compress the data by utilizing data types with lower precision or by rounding off values.
Data Transformation: You can use Power Query Editor in Power BI to transform your data before loading it into the dataset. For example, you can unpivot the columns so that each data point is represented in a single row, rather than having multiple columns for each frequency value. This can help reduce the number of columns and make the dataset more efficient.
Data Modeling Techniques: Power BI offers various modeling techniques, such as creating calculated columns or measures, to perform calculations or aggregations on the fly. By utilizing these techniques, you can avoid duplicating data and reduce the overall storage space.
HI Angel_S_M ,
check below points:
Data Aggregation: Instead of storing each individual data point, you can aggregate the data at a higher level. For example, you can calculate the average, minimum, maximum, or any other relevant summary statistics for each frequency range or time period. This can significantly reduce the number of rows and still provide meaningful information for plotting the curve.
Data Compression: Power BI supports data compression techniques, which can help reduce the storage space and improve processing speed. Power BI automatically applies compression algorithms to minimize the size of your dataset. You can also manually compress the data by utilizing data types with lower precision or by rounding off values.
Data Transformation: You can use Power Query Editor in Power BI to transform your data before loading it into the dataset. For example, you can unpivot the columns so that each data point is represented in a single row, rather than having multiple columns for each frequency value. This can help reduce the number of columns and make the dataset more efficient.
Data Modeling Techniques: Power BI offers various modeling techniques, such as creating calculated columns or measures, to perform calculations or aggregations on the fly. By utilizing these techniques, you can avoid duplicating data and reduce the overall storage space.