Data Exports
Data Exports push your trace and observation data to your own cloud storage on a schedule, so you can warehouse it, run your own analytics, or feed downstream pipelines.
Configure exports in project settings. You'll provide a storage destination and credentials; data is written there on the schedule you set.
Destinations
- Amazon S3
- Google Cloud Storage (GCS)
- Azure Blob Storage
Formats
- Parquet — columnar, ideal for warehousing/analytics
- JSON / NDJSON — row-oriented, easy to process line by line
Configuring an export
Set the destination
Choose S3 / GCS / Azure and enter the bucket/container, path prefix, region/endpoint, and credentials (encrypted at rest).
Choose format and schedule
Pick the file format and how often the export runs (e.g. hourly or daily), plus the write mode.
Test and enable
Test the connection, then enable. You can trigger an export on demand and review export history.
Exports contain your raw trace data, which may include sensitive content. Restrict the destination bucket, use least-privilege credentials, and align retention with your Data Privacy policy.
Next steps
- API Reference — pull data programmatically instead.
- Data Privacy — handling exported data responsibly.