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Overview

An export writes one dataset to one of your destinations as Parquet files, on a schedule you choose. This guide covers the create flow that’s common to every dataset; each dataset page documents the columns and any dataset-specific settings.

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Prerequisites

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Create the export

1

Open Data Exports

In the dashboard, go to Integrations → Data Exports and select Create export.
2

Name the export

Give it a recognizable name, for example “Touchpoints to warehouse”.
3

Choose a destination

Select the destination to write to. Files land in that destination’s bucket.
4

Set a path prefix (optional)

Optionally set a path prefix (for example cometly/). Files are written under this path within the bucket.
5

Pick a dataset

Choose the dataset to export. Each dataset has its own columns and quirks — see the dataset reference.
6

Choose the columns

Pick which columns the Parquet files carry from the dataset’s catalog. Locked columns are always included; the rest are optional. Your selection is frozen at creation (see below).
7

Set the cadence and window

Choose how often the export runs and how much data each run covers:
  • One-time — runs once. Pick an absolute start and end date.
  • Daily — runs once a day at a scheduled hour you choose. Pick a trailing window preset (up to 90 days).
  • Hourly — runs every hour. Pick a trailing window preset (up to 14 days; up to 7 days for ad-analytics datasets).
See Cadence and windows for how trailing windows behave.
8

Configure dataset-specific settings

Some datasets ask for more at creation — the Attributed conversions dataset takes an attribution model, window, type, and optional source filter; the Contacts dataset asks how custom-field columns should be named. Most datasets need only a column selection.
9

Create

Select Create export. For recurring cadences the first run is scheduled; for one-time it runs right away. Cometly writes the data to your destination and records each run in the export’s run history.
Each export run is recorded with its status and timing. Open an export to view its run history.

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Exports are immutable

Once created, an export’s name is the only thing you can change. To alter the cadence, window, destination, dataset, or columns, delete the export and create a new one. This keeps every run’s output faithful to the configuration that produced it — a run’s columns and shape never drift underneath your pipeline.

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File layout

Files are written to your bucket under a Hive-style partitioned path: 
<prefix>/<dataset>/space_id=<id>/exported_date=<YYYY-MM-DD>/run_id=<ULID>/
  ├── <parquet files>
  └── _SUCCESS
  • <dataset> is the dataset’s path segment — touchpoints, raw_conversions, raw_hits, attributed_conversions, contacts, companies, or <channel>_ad_analytics (for example facebook_ad_analytics). Each dataset page lists its segment.
  • exported_date is the date the export ran (not the event date) and is sortable, so your pipeline can always find the most recent export.
  • run_id makes every run unique, so runs never overwrite each other and retries are safe.
  • _SUCCESS is written last. Only ingest a run folder once its _SUCCESS marker is present, so you never read a partially written run.

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Loading the data

Point your ingestion tooling — Snowflake, BigQuery, Databricks Auto Loader, Athena, and others — at the …/<dataset>/space_id=<id>/ prefix to pick up new partitions automatically.
Each recurring run re-exports the entire window, not just new rows — so the most recent exported_date is the current snapshot of that window. Deduplicate on a stable key (such as the row’s id, or the id plus metric date for ad analytics) when loading.