Need Advice: Optimise Datasheet Storage Space (Reduce No. of Rows)

[Angel_S_M]

I have a datasheet on Power BI, reading test parameters of various devices. However, there are so many values taken that the number of rows blows up to 6 digits. For example, the sample data extract below is only for one test, for two serial numbers, over a limited frequency range.

Is there a method of optimising the raw data to keep the row/column number as low as possible with ability to be plotted as an Output vs. Frequency curve, without aggregating the datapoints or otherwise changing the raw data values? Specifically I want to decrease the number of cells used so that storage space and processing speed is reduced. 

For example, would it be possible to plot a curve from two columns with comma separated values within a cell? E.g. plotting if all the Frequency values for SerialNumber ABC01 were in csv format in one cell, and the same for Output.

Otherwise, is there a manual transformation method such as using Pivoting that can reduce the space used?

Thank you, looking forward to any solutions! 😃 

SerialNumber	Frequency	Output
ABC01	2000	109.07
ABC01	1900	107.84
ABC01	1800	106.93
ABC01	1700	106.2
ABC01	1600	105.03
ABC01	1500	104.39
ABC01	1400	103.97
ABC01	1300	103.76
ABC01	1200	103.67
ABC01	1100	103.41
ABC01	1000	103.14
ABC01	900	102.85
ABC01	800	102.46
ABC01	700	102.44
ABC01	600	102.18
ABC01	500	102.23
ABC01	400	102.27
ABC01	300	102.75
ABC01	200	102.71
ABC02	2000	109.76
ABC02	1900	108.51
ABC02	1800	107.68
ABC02	1700	106.9
ABC02	1600	105.73
ABC02	1500	105.08
ABC02	1400	104.65
ABC02	1300	104.42
ABC02	1200	104.41
ABC02	1100	104.09
ABC02	1000	103.83
ABC02	900	103.6
ABC02	800	103.15
ABC02	700	103.09
ABC02	600	102.87
ABC02	500	102.92
ABC02	400	102.9
ABC02	300	103.43
ABC02	200	103.46

[vs_7 · Accepted solution]

HI @Angel_S_M ,

check below points:

1. 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.

2. 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.

3. 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.

4. 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.