Which table structure is most efficient for Power BI

[Sonu88]

We are designing a Fact table for use in Power BI and need guidance on the best approach for handling large datasets. Should we opt for a wide Fact table, where each KPI or metric (such as sales, revenue, etc.) is represented as a separate column,

Product ID	Region ID	Revenue	Sales	Backlog
1	11	100	200	50
2	22	200	300	60
3	33	300	350	70

or a long Fact table, where each metric is stored as a separate row, please see the below structure?

Product ID	Region ID	KPI	Measure
1	11	Revenue	100
1	11	Sales	200
1	11	Backlog	50
2	22	Revenue	200
2	22	Sales	300
2	22	Backlog	60
3	33	Revenue	300
3	33	Sales	350
3	33	Backlog	70

The Fact table design should prioritize future scalability, minimal maintenance, and optimal performance within Power BI.

The required measures what we are going to show are MTD, QTD, YTD, YOY comparision, 2 years trend etc.

[miTutorials · Accepted solution]

Would suggest to go with Wide table and below are couple of reasons to support.

1. Enables direct use of time intelligence functions like YTD, QTD, and YOY on individual KPI columns.

2. Fewer rows and better columnar compression improve model efficiency and query speed.

3. Supports straightforward creation of charts, trends, and comparisons without any extra filtering logic.

[v-dineshya · Accepted solution]

Hi @Sonu88 ,

Thank you for reaching out to us on the Microsoft Fabric Community Forum.

Thank you @miTutorials  for sharing valuable information.

Hi @Sonu88 , In addition to @miTutorials  , i am adding some points.

The wide table format is generally more efficient and scalable.

Better performance: VertiPaq (Power BI’s in-memory engine) compresses numeric columns very efficiently. With fewer rows and more numeric columns, Power BI loads and aggregates faster.

Easier modeling: Each metric is a simple numeric column; you can use standard aggregation methods (SUM, AVERAGE, etc.) without needing to filter or pivot data first.Relationships and measures are easier to manage when each KPI is a separate column.

Better for Trend Analysis: Calculating trends across time (e.g., YOY, 2-year trends) is more straightforward.

Visual flexibility: Directly use any metric on axes, values, or tooltips in visuals without pivoting or unpivoting.

If my response has resolved your query, please mark it as the "Accepted Solution" to assist others. Additionally, a "Kudos" would be appreciated if you found my response helpful.

Thank you

[v-dineshya]

Hi @Sonu88 ,

If my response has resolved your query, please mark it as the "Accepted Solution" to assist others. Additionally, a "Kudos" would be appreciated if you found my response helpful.

Thank you

[v-dineshya]

Hi @Sonu88 ,

We haven’t heard from you on the last response and was just checking back to see if you have a resolution yet.do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.

Thank you.

[v-dineshya]

Hi @Sonu88 ,

We haven’t heard from you on the last response and was just checking back to see if you have a resolution yet.do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.

Thank you.

[Sonu88]

What other experts are saying?

[miTutorials · Accepted solution]

Would suggest to go with Wide table and below are couple of reasons to support.

1. Enables direct use of time intelligence functions like YTD, QTD, and YOY on individual KPI columns.

2. Fewer rows and better columnar compression improve model efficiency and query speed.

3. Supports straightforward creation of charts, trends, and comparisons without any extra filtering logic.