Find the rightsources.
Agents search datasets and public sources, then compare coverage, history, fields, and update cycles.
Let agents find the sources, clean and join the data, and keep it updated. Download the full Parquet for training or query the latest rows through one API call.
Daily maximum temperature for Prague and Berlin from 2010 to today.
The question
question = {
'grain': 'One row per city and date',
'primary_key': 'city + date',
}Sources
41 candidates checked · 3 cover both cities for the whole range
Build
Column analytics
Deployed
GET/v1/prague-berlin-daily-maxima
Start with a data question. The result is a clean dataset that stays current and is ready to download or query.
Agents search datasets and public sources, then compare coverage, history, fields, and update cycles.
Agents connect to open and keyed APIs, then read data from CSV, JSON, ZIP, PDF, and GRIB files.
Agents fix names, types, units, dates, and time zones from every source before joining the tables on the right keys.
Agents check when new data appears and suggest a schedule. The first run fills your date range, then the dataset stays updated.
Download the full dataset as Parquet, or query the latest rows as JSON through the same API.
Agents search existing datasets and public sources. They compare coverage, history, fields, format, and update frequency, then show which sources they would use.
Agents configure built-in readers for each API, file, and document. The pipeline downloads and parses the data, then runs again whenever the sources publish something new.
Each column gets a clear name, type, and unit. Timestamps are converted to UTC. Every conversion is checked before the tables are joined. For temperatures, that includes both the scale and the offset.
Give each column a clear name, data type, and documented unit.
Convert measurements with the exact scale and offset required by their units.
Match rows on the right keys. A join is refused when declared units disagree.
Verify row counts, date ranges, required values, and missing data.
You choose the date range. Agents check when new data actually appears and suggest an update schedule. The first run fills the range, then the dataset stays updated automatically.
Download the full history as Parquet to train models that predict real-world events, then query the latest rows through an API when those models need fresh data.
Invite teammates and control who can build, manage, share, or only view datasets. Keep source credentials secure across the organization, and choose which datasets stay private, are shared by link, or become public.
Assign Owner, Admin, Editor, or Viewer roles. Changes and removals apply on the next request.
Keep datasets inside the workspace, share them by link, or publish them when you are ready.
Credentials stay in Secret Manager and are released only to the exact ingest run that needs them.
Agents find the sources, clean and join the data, and keep it updated. Download the full history or query the latest rows through one API.