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The CSV File data source accepts .csv files uploaded directly through the interface. No external service is contacted at any point — uploaded files are parsed and stored in a dedicated cache table. Connecting it brings manually curated datasets, mapping tables, conversion targets, or ad-platform exports into Adriel dashboards without wiring a recurring sync.

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Before you connect

The following are required:
  • One or more .csv files. Other formats, such as XLSX, are rejected at upload time.
  • A consistent column structure across all files in the same data source — the same header names and the same column count.
Two file requirements must be met before connecting, or the upload is rejected:
  • The file extension is limited to UTF-8 encoded .csv format. If the file is not suitable, change the format or extension in the file settings before connecting.
  • The column header (first row) must not contain empty columns.

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Connect CSV File

1

Open the data source

On the Data Sources page, search for CSV File and select it.
2

Name the data source

The name field defaults to default. Keep it and click Submit, or replace it with a distinguishable name for this upload and click Submit.
3

Set optional fields

Set an Alternative Channel Name to control the display name for the channel. Set a File Name to Filter to restrict the data source to files matching a name pattern.
4

Choose an update method

Select how new uploads are merged. Append (default) adds rows and stamps them with file_id and filename provenance. Upsert merges on a declared primary-key column. Replace swaps the cache table contents with each new upload.
5

Upload the files

Drag and drop one or more .csv files into the upload area, or browse to select them. Each file is processed individually — if one upload fails, the others continue. The first upload establishes the column schema and triggers type inference.
6

Finish

Close the settings tab. The connection saves and connects automatically once the uploads complete, and the data source is immediately available for dashboards.
To add or remove files later, open the data source settings from the Connected data sources list, add or delete files, then close the tab — changes save automatically. Every later upload must match the original column structure; files with different headers are rejected, so create a separate CSV File data source for a differing layout.

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What gets imported

Column types are inferred on the first upload from sampled rows. Numeric columns become metrics and text columns become breakdowns. Column names matching *_id, percent, pct, or % are forced to text to preserve formatting. Type inference runs once and persists for the lifetime of the data source. For the complete field list and limits, see the CSV File data reference.

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Troubleshooting

The file’s headers or column count do not match the data source’s established schema. Reformat the file to match, or create a separate CSV File data source for the differing layout.
Type inference runs once during the first upload and persists. If the original sample included non-numeric values, or the column name matches an ID or percentage pattern, the column stays text. Re-create the data source after fixing the source file to change the inferred type.
Browser-based uploads are bounded by browser memory. For files in the high hundreds of megabytes or larger, use Amazon S3, File from SFTP, or Custom URL Import instead.

CSV File data reference

Fields, type inference, and limits for the CSV File data source.

CSV File FAQs

Common questions and expected behaviors for the CSV File data source.

How to connect Custom URL Import

Pull CSVs from a URL on a schedule instead of uploading by hand.

How to connect Amazon S3

Connect larger or scheduled file batches from an S3 bucket.