CSV (AWS S3) as a destination

1. Add your CSV (AWS S3) access

In the Destinations tab, click on the “Add destination” button located on the top right of your screen. Then, select the CSV (AWS S3) option from the list of connectors.
Click Next and you’ll be prompted to add your access:

Bucket name: The AWS bucket name where you want to store the CSV files.
Output folder path: Folder path where to store output files inside the S3 bucket. Leave empty if the file should be stored in the bucket’s root folder.
Stream name: A custom name for the stream that can be used to compose the file name.
File naming format: The format to be used to generate the output files name. These are the variables that can be used: - {stream_name} - {datestamp} - {timestamp} - {batch_id} - {batch_index}.
Maximum CSV file size (MB): (optional) Define a maximum size for each CSV file. In case your input data is bigger than that, multiple files will be generated respecting the maximum size defined.

The next step is letting us know which data you want to send to the spreadsheet. Select the layer and then the desired table.

Tip: The table can be found more easily by typing its name.

Describe your destination for easy identification within your organization. You can inform things like what data it brings, to which team it belongs, etc.
To define your Trigger,consider how frequently you need to have the data updated on the destination (every day, once a week, only at specific times, etc.).
Click Done.

Once completed, you’ll receive confirmation that your new destination is set up!
You can view your new destination on the Destinations page. Now, for you to be able to see it on your CSV, you have to wait for the pipeline to run. You can monitor it on the Destinations page to see its execution and completion. If needed, manually trigger the pipeline by clicking on the refresh icon. Once executed, your data should be seen on the CSV.

If you encounter any issues, reach out to us via Slack, and we’ll gladly assist you!