> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-postgresql-tls-support.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Upload files to Cloud
> Learn how to upload files to Cloud
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ClickHouse Cloud provides an easy way to import your files and supports the
following formats:
| Format |
| ------------------------------- |
| `CSV` |
| `CSVWithNamesAndTypes` |
| `CSVWithNames` |
| `JSONEachRow` |
| `TabSeparated` |
| `TabSeparatedWithNames` |
| `TabSeparatedWithNamesAndTypes` |
From the Cloud homepage, select your service as shown below:
If your service is idle you will need to wake it.
Select `Data sources` in the left hand tab as shown below:
Next select `Upload a file` on the right side of the data sources page:
A file dialogue will pop up allowing you to select the file that you wish to
use to insert data into a table on your Cloud service.
Once the file has uploaded you will be able to configure the table where you want
to insert the data to. A preview of the table with the first three rows is shown.
You can now select a destination table. The options are:
* a new table
* an existing table
You can specify which database you want to upload the data to, and in the case of
a new table, the name of the table that will be created. You will also be able to select the sorting key:
Columns read from the file are shown as `Source field`s and for each field, you
can change:
* the inferred type
* the default value
* whether to make the column [Nullable](/reference/data-types/nullable) or not
**Excluding fields**
You can also remove a field if you don't want to include it in the import
You can specify the type of table engine that you want to use:
* `MergeTree`
* `ReplacingMergeTree`
* `SummingMergeTree`
* `Null`
You can specify a partitioning key expression and primary
key expression.
Click `Import to ClickHouse` (shown above) to import the data. The data import will be queued as
indicated by the `queued` status badge in the `Status` column as shown below. You can also click
`Open as query` (shown above) to open the insert query in the SQL console. The query will insert
the file which was uploaded to an S3 bucket using the `URL` table function.
If the job fails you will see a `failed` status badge under the `Status` column of
the `Data upload history` tab. You can click `View Details` for more information
on why the upload failed. You may need to modify the table configuration or clean
the data based on the error message for the failed insert.