Architecting Historical Data Migration Pipelines from PureConnect Interaction Recorder
What This Guide Covers
This guide details the architectural pattern for constructing an ETL pipeline that extracts historical interaction metadata and media references from a legacy Genesys PureConnect Interaction Recorder environment and ingests them into Genesys Cloud CX for long-term retention. Upon completion, you will possess a production-grade data ingestion framework capable of normalizing legacy binary formats into Cloud-native JSON schemas while maintaining strict audit trails for compliance verification.
Prerequisites, Roles & Licensing
Before initiating the pipeline construction, verify the following environmental constraints and access controls. Failure to secure these prerequisites will result in immediate API rejection or incomplete data migration.
- Licensing Tier: Requires Genesys Cloud CX Architect license with Data Export add-on enabled. PureConnect side requires a valid Interaction Recorder license for export permissions.
- Granular Permissions: The service user executing the pipeline must possess the following OAuth scopes and API roles:
dataexport.read(To verify destination storage configuration)dataexport.write(To trigger export jobs if utilizing Cloud-side extraction)interaction.export(To access interaction metadata)telephony.settings.edit(For S3 bucket configuration if using external storage)
- API Access: Service Application credentials (Client ID and Client Secret) generated within the Genesys Cloud Admin Portal under Developer Console > OAuth.
- External Dependencies:
- Object Storage: An AWS S3 bucket or Azure Blob Storage container configured for immutable data retention. This replaces PureConnect local file storage.
- ETL Compute: A compute cluster (e.g., AWS Lambda, Kubernetes Pod) capable of running Python 3.10+ scripts for JSON transformation.
- Network Security: Outbound connectivity to
api.mypurecloud.comand the configured Object Storage endpoint must be whitelisted in the firewall.
The Implementation Deep-Dive
1. Schema Normalization and Metadata Extraction
The first architectural challenge involves mapping the PureConnect Interaction Database schema to the Genesys Cloud Interaction JSON structure. PureConnect stores interaction data in a proprietary SQL format with specific columns for call direction, queue time, and disposition codes. Genesys Cloud expects a normalized JSON payload conforming to the Interaction Schema v2.
Begin by querying the legacy database to retrieve the interaction logs. You must implement a transformation layer that maps legacy fields to the Cloud schema. The critical fields include direction, startTime, endTime, duration, and contactType.
Construct the extraction script to iterate through the legacy rows. For every record, generate a JSON object matching the following structure:
{
"id": "legacy-pc-id-12345",
"direction": "inbound",
"startTime": "2023-01-15T08:30:00Z",
"endTime": "2023-01-15T08:35:22Z",
"duration": 322,
"contactType": "call",
"queue": {
"id": "legacy-queue-id",
"name": "Support"
},
"reasonForTermination": "normal",
"agentId": "legacy-agent-uuid"
}
You must ensure the id field in this payload is unique and does not conflict with existing Cloud interaction IDs. Use a UUID v4 generator for the internal mapping ID, but retain the legacy ID in a custom attribute field to preserve referential integrity for downstream analytics.
The Trap: Do not attempt to force the legacy interaction ID into the id field of the Genesys Cloud payload without validation. If the legacy ID contains characters that violate Cloud schema constraints (such as special symbols), the API request will fail with a 400 Bad Request error, halting the entire pipeline. Instead, map the legacy ID to a custom attribute field named x_legacy_id.
The architectural reasoning for this separation is that Genesys Cloud uses the id field for internal event correlation and stream processing. Overwriting these IDs breaks the linkage between the historical data and future real-time events if you attempt to merge streams later. By using a custom attribute, you preserve the legacy identity without compromising Cloud internal state consistency.
2. Media File Handling and Secure Ingestion
PureConnect Interaction Recorder stores media files (recordings) on local disk drives or network shares, typically in .wav or proprietary binary formats. Genesys Cloud CX expects recordings to reside in an Object Storage bucket (S3/Azure) with specific metadata headers. The migration pipeline must handle the physical transfer of these binary files and their associated metadata links.
Do not attempt to upload media files directly via the Interaction API endpoint, as this is designed for small attachments. Instead, utilize the Presigned URL mechanism provided by Genesys Cloud or your external storage provider. This ensures that large binary payloads do not traverse the public internet in a way that causes timeout errors or bandwidth saturation.
Implement the following workflow within your ETL script:
- Query the media file path from the PureConnect export metadata.
- Request a presigned upload URL from the target storage bucket endpoint via the Cloud API
POST /api/v2/conversations/contacts/{contactId}/recording. - Upload the binary file using the HTTP PUT method with the signed URL in the request header.
- Capture the resulting public or private URL returned by the storage service.
- Attach this URL to the interaction metadata JSON payload created in Step 1.
The JSON payload for the recording attachment must include the contentType and url. Ensure the url points to the secure object location and not a local file path.
{
"attachments": [
{
"contentType": "audio/wav",
"url": "https://s3.amazonaws.com/your-bucket/recordings/rec-12345.wav"
}
]
}
The Trap: A common failure mode occurs when the media file upload succeeds, but the interaction metadata update fails to link the recording URL. This happens if the script does not wait for the storage service to confirm the write operation before attempting to reference the URL in the API call. Storage services often have eventual consistency. If you query the URL immediately after the PUT request without checking status, the interaction record may reference a non-existent file path, resulting in playback errors during audit verification. Implement a retry logic with exponential backoff to verify file existence before updating the interaction metadata.
The architectural decision here prioritizes data integrity over speed. Waiting for storage confirmation adds latency but prevents orphaned records that violate compliance retention policies.
3. Ingestion into Genesys Cloud via Data Import API
Once metadata and media references are normalized, the final step is ingesting the data into Genesys Cloud. You will use the Data Export API in reverse or the Interaction Import capabilities depending on your licensing tier. For historical migration, the POST /api/v2/interactions endpoint is generally used to create records if the environment supports bulk creation. However, for large-scale historical loads, the recommended pattern involves uploading a CSV or JSON bundle to Cloud Storage and triggering a batch import job via the Data Export API.
Prepare the ingestion payload by batching the normalized interaction objects into groups of 100 to optimize throughput. Use the application/json content type header. Include the X-Request-ID header for correlation in case of failure.
Example request body for a batch ingest:
{
"interactions": [
{
"id": "new-cloud-id-001",
"direction": "inbound",
"startTime": "2023-01-15T08:30:00Z",
"endTime": "2023-01-15T08:35:22Z",
"duration": 322,
"contactType": "call",
"reasonForTermination": "normal"
},
{
"id": "new-cloud-id-002",
"direction": "outbound",
"startTime": "2023-01-15T09:00:00Z",
"endTime": "2023-01-15T09:02:15Z",
"duration": 135,
"contactType": "call",
"reasonForTermination": "abandoned"
}
]
}
Execute the request using a standard HTTP client with OAuth 2.0 Bearer Token authentication. The token must be refreshed periodically to avoid 401 Unauthorized errors during long-running migrations.
The Trap: Do not attempt to import interactions with timestamps in the past without enabling the Historical Data Import flag in the environment settings. By default, Genesys Cloud validates interaction start times against the current server time to prevent replay attacks or data tampering. If you submit historical records without this configuration enabled, the API will reject the request with a 403 Forbidden error. You must contact Genesys Support or enable the specific allow-historical-import setting in the Data Export configuration page before starting the pipeline.
The architectural reasoning behind this restriction is data integrity and security. Allowing arbitrary historical data to be written to the system could allow bad actors to inject false interaction logs that skew reporting metrics. Enforcing this flag ensures that only authorized migration processes can bypass the timestamp validation logic.
Validation, Edge Cases & Troubleshooting
After the pipeline completes execution, you must validate the integrity of the migrated data before decommissioning the legacy PureConnect system. This phase involves reconciliation of record counts and media playback verification.
Edge Case 1: Timestamp Drift in Timezones
The Failure Condition: Historical records appear to have incorrect durations or end times when viewed in the Cloud Analytics dashboard compared to the source logs.
The Root Cause: The PureConnect system stored timestamps in a local timezone (e.g., America/New_York) without UTC offset information, while Genesys Cloud stores all timestamps in UTC. If the migration script assumes UTC for legacy data that was actually local time, every record will be shifted by the offset value, causing duration calculations to fail and audit trails to be inaccurate.
The Solution: Implement a timezone detection logic in the ETL script. Check the TimeZone column in the PureConnect export metadata. If the source data is ambiguous, default to UTC only after explicit confirmation from the compliance team. Always store the original raw timestamp string in a custom attribute field for forensic review.
Edge Case 2: Recording Playback Failure
The Failure Condition: Users report that historical recordings play as silent or fail with a 404 error when accessed via the Cloud UI.
The Root Cause: The object storage bucket permissions were not configured to allow public read access, or the presigned URL expired before the migration completed. Additionally, the MIME type header may be incorrect (e.g., audio/mpeg instead of audio/wav).
The Solution: Verify the Object Storage Bucket Policy allows GET requests for the specific user group. Ensure the upload script sets the Content-Type header to match the file extension exactly. Use a playback test script that iterates through 50 random migrated interaction IDs and attempts to fetch the recording stream to confirm accessibility before marking the batch as complete.
Edge Case 3: Duplicate Interaction IDs
The Failure Condition: The import process reports duplicate key errors during the ingestion phase.
The Root Cause: The legacy system generated non-unique identifiers for interactions that were split across multiple database shards, or the script failed to increment the mapping counter between batches. This results in multiple Cloud interaction records sharing the same primary key.
The Solution: Implement a deduplication check at the start of the pipeline against the id field. Use a set data structure in memory to track processed IDs. If a duplicate is detected, log the conflict and skip the record rather than halting the process. Ensure the mapping table between Legacy ID and Cloud ID is persisted to a database for auditability.