Elaine supports data exchange from individual real-time changes to large scheduled imports and exports. APIs provide record-level control; SIEIS provides asynchronous SFTP processing for file-based, high-volume workloads.
Choose a data exchange pattern
| Requirement | Recommended pattern | Why |
|---|---|---|
| Change one record and receive immediate technical feedback | RESTful or HTTP API | Fine-grained synchronous control |
| Apply many similar operations with API semantics | Supported bulk API mode | Reduces connection and authentication overhead |
| Import a large contact or campaign dataset | SIEIS import | Efficient asynchronous file processing with operational status and rejected-file handling |
| Deliver response or customer data to another platform on a schedule | SIEIS export | Recurring, restartable delivery of consolidated files |
| Trigger business processing without waiting for all data actions to finish | Inbound event plus Elaine automation | Decouples the source system from downstream orchestration |
SIEIS for large batches
SIEIS exchanges import and export files through SFTP using key-based access. It is suitable when data can be bundled, processing can be asynchronous, and the integration requires clear operational handling for uploaded, processing, archived, rejected, and status information.
Typical uses include contact master data, campaign contact data, suppression or permission-related updates, content or personalization data, and exports of response or processing data. Supported operations and file conventions depend on the configured import or export process.
Scale and scheduling
File-based processing avoids one network request per record and is the preferred pattern for many large batch workloads. With suitable file partitioning, processing configuration, and contracted capacity, SIEIS or purpose-built high-throughput API flows can process millions of records per hour.
Agree the delivery cadence, maximum file size, compression, character encoding, key fields, duplicate rules, parallelization, completion target, retention, and recovery process before implementation.
Data integrity and governance
- Use stable identifiers and define how existing, missing, duplicate, and deleted records are handled.
- Validate schema, encoding, required fields, values, and record counts before processing.
- Define consent, data-source, suppression, and deletion behavior for contact data.
- Reconcile accepted, rejected, and processed records after every batch.
- Protect files and logs according to their data classification and retention requirements.