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# Ways to Load Data into a Microsoft Fabric Lakehouse: Upload Files

**[C\# Corner](https://daily.dev/sources/csharpcorner)** · 5 min read · 3 upvotes · 0 comments

## Summary

Uploading files is one of the simplest options for moving small local datasets into a Microsoft Fabric Lakehouse without building a pipeline. Files land in the Files section stored in OneLake, but uploading alone does not create a Lakehouse table. To query the data, select 'Load to Tables → New table' on the file, which creates a Delta table based on the selected settings. The original CSV stays in Files while the new table can be queried via the SQL analytics endpoint, read with Spark notebooks, or used for Power BI reports. Larger or recurring ingestion is better handled with Data pipelines, Dataflow Gen2, or notebooks, while a OneLake shortcut suits data that already exists elsewhere and should not be copied.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.c-sharpcorner.com/article/ways-to-load-data-into-a-microsoft-fabric-lakehouse-upload-files>

## Questions this post answers

### Does uploading a CSV file to a Microsoft Fabric Lakehouse automatically create a table?

No, uploading a file only places it in the Files section of the Lakehouse, backed by OneLake storage. It remains an unstructured file until you explicitly convert it. To create a queryable Delta table, select the file's ellipsis menu, choose 'Load to Tables → New table', name the table, and select Load, which infers a schema and creates the Delta table.

_daily.dev surfaces practical walkthroughs like this for engineers piecing together Fabric ingestion workflows._

### When should I use Upload Files instead of Data pipelines in Microsoft Fabric?

Upload Files is best for relatively small datasets brought in manually from a local computer, without building a pipeline. For larger datasets, recurring ingestion, or automated transformations, Data pipelines, Dataflow Gen2, or notebooks are better suited. If the data already lives in another supported storage location, a OneLake shortcut can avoid copying it entirely.

_Developers weighing Fabric ingestion options can compare tradeoffs like these on daily.dev before committing to a pipeline._

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---

Tags: [#big-data](https://daily.dev/tags/big-data), [#data-engineering](https://daily.dev/tags/data-engineering), [#microsoft-fabric](https://daily.dev/tags/microsoft-fabric)

[View this post on daily.dev](https://daily.dev/posts/ways-to-load-data-into-a-microsoft-fabric-lakehouse-upload-files-e0pp8tzi4)

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