Data Import

Upload files, schedule URL fetches, activate data packages, or run scrapers. Their data goes through the shared import pipeline, using the dataset’s schema, duplicate-detection, and geocoding settings.

The Pipeline

Imports use the following processing stages. Review checks can pause an import when an issue needs attention; approval is not required for every import.

Source (file, URL, or scraper output)
  → Detect sheets and datasets
    → Analyze for duplicates
      → Detect schema (field types, dates, locations)
        → Validate schema (pause for review when required)
          → Create schema version
            → Resolve locations and geocode addresses as configured
              → Create events

Supported Formats

Schema Detection

TimeTiles analyzes your data and identifies:

• Field types — strings, numbers, dates, booleans
• Date fields — timestamps, start dates, and end dates when present
• Location fields — addresses, coordinate pairs, or separate lat/lng columns
• Enum fields — columns with few unique values (become interactive filters)

In the upload wizard, you review and adjust mappings before starting the import. If your file uses a single combined coordinate column, confirm whether the values are lat,lng or lng,lat before starting. Background imports can pause for review if detection finds an unresolved issue.

Geocoding

Text addresses can be converted to coordinates using the instance’s configured providers, with fallback and caching. Valid coordinates in your data can be used directly. See Geocoding Configuration for provider setup.

Deduplication

When duplicate detection is enabled, TimeTiles checks for duplicates within the incoming data and against existing events in the target dataset. The current ID strategies are:

• External ID — use a unique identifier field from your data
• Content hash — identify rows by their transformed content, optionally excluding configured fields
• Auto-generate — assign new IDs without duplicate detection

Detected duplicates can be skipped or used to update existing events, depending on the dataset’s duplicate strategy.

Schema Evolution

TimeTiles tracks schema versions as your data changes. Whether a change is accepted automatically, pauses for approval, or fails validation depends on the source’s schema mode and the dataset’s settings.

Troubleshooting