利用できる列数

[KeikoY]

各データソースから取得するデータの行数が10,000件ぐらいのとき、DirectQuery、インポートそれぞれの最大列数は125で合っていますか？また、PowerBIDeskTop上で利用できる列数はだいたいどれぐらいでしょうか。

[Anonymous]

Hi @KeikoY ,

Just following up to see if the solution provided was helpful in resolving your issue. Please feel free to let us know if you need any further assistance.

If the response addressed your query, kindly mark it as Accepted Solution and click Yes if you found it helpful — this will benefit others in the community as well.

Best regards,

Prasanna Kumar

[Anonymous]

Hi @KeikoY,

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

When working with around 10,000 rows per data source in Power BI, using Import mode allows more flexibility, with no strict column limit—though best practice is to keep tables under 250–300 columns for performance. In DirectQuery mode, the practical limit is lower, typically around 100–120 columns, and while 125 columns may work, it approaches the upper edge and could impact performance depending on data types and query complexity. In Power BI Desktop, there's no fixed limit on total columns across the model, but performance depends on overall model size, relationships, and DAX usage. To optimize performance, it's recommended to avoid wide tables, remove unused columns, and use Import mode or aggregated views where possible.

If you find this response helpful, please consider marking it as the accepted solution and giving it a thumbs-up to support others in the community.

Thank you & regards,
Prasanna Kumar

[rajendraongole1 · Accepted solution]

Hi @KeikoY  - If possible next time please post in english, as of now i have used conversion.

10,000 行を扱う場合、以下の点に留意してください。

リアルタイムまたはほぼリアルタイムのデータが必要でない限り、インポートの方が効率的です。

DirectQuery を使用している場合は、以下の点をお試しください。

未使用の列を減らす。

目的（ファクト/ディメンションモデリング）別にテーブルを分割する。

集計テーブルを使用する。

ソース（ビューやインデックスなど）でクエリを最適化する。

English: 

Since you're working with 10,000 rows, here’s what to consider:

Import is more efficient unless you need real-time or near real-time data.

If you're using DirectQuery, try:

Reducing unused columns.

Splitting tables by purpose (fact/dimension modeling).

Using aggregation tables.

Optimizing queries at the source (e.g., views or indexes).

I hope this helps.