Supplier updates with a clear history.
Plan supplier data imports with reusable field mapping, validation, change detection and controlled updates to hotel mapping relationships.
Supplier data connectivity brings files or feeds into a consistent processing workflow. A reliable import preserves supplier identifiers, validates the input, distinguishes full snapshots from partial updates and records what changed before publishing approved mapping updates.
Give every record
a clearer role.
Practical controls for teams managing supplier data and mapping decisions.
Reusable field mapping
Define how supplier columns map to your schema, with validation rules and a version for each import profile.
Inspect changes first
Separate new, changed, unchanged and invalid records so operators can understand the impact of an upload.
Handle missing records safely
Apply absence rules only to a validated full snapshot for the declared scope. A partial file cannot establish a deletion.
Keep updates reversible
Maintain import history and previous approved relationships so a problematic batch can be investigated and corrected.
How supplier connectivity fits together.
Declare the feed
Record supplier, source, schema version, file scope and whether the input is a full snapshot or a delta.
Validate and stage
Check format, required identifiers, duplicate source keys and geographic fields before accepting records.
Compare versions
Detect changes with a repeatable record fingerprint and classify identity conflicts separately from content edits.
Apply approved changes
Publish accepted updates as a new version while retaining rejected rows, review tasks and a batch summary.
Keep the context
with the connection.
The solution scope describes a production approach. The working website demo focuses on hotel matching; supplier APIs and other production modules require integration.
- Store the raw source, batch identifier, received time, source timestamp and schema version.
- Quarantine invalid rows and distinguish a failed download from an intentionally empty complete snapshot.
- Use idempotent processing, staged approval and versioned publication in a production implementation.
A few good questions.
Understand the data, the decisions, and the next step.
Explore all FAQsWhat is the difference between a snapshot and a delta file?
A snapshot represents the complete declared dataset at a point in time. A delta contains only changes. Missing rows in a delta say nothing about whether those properties are still active.
Should a missing hotel be deleted automatically?
No. First verify that the file is complete for its declared scope and check the supplier's status rules. Any suspension should apply to that supplier profile; the master property and other supplier relationships remain separate.
What if the same supplier file is uploaded twice?
The import design should recognize a previously processed batch or unchanged records. Replaying an identical file should not create duplicate relationships or repeat completed actions.
Does this website connect to supplier APIs?
The website presents the connectivity solution design. The browser demo uses illustrative data. A production supplier connection requires the supplier's access, agreed schema, authentication and implementation.
Connected solutions
A clearer identity starts here.
Explore the mapping workflow. Build a brief around your data.