Implementing Bulk Recording Migration Tools for Platform Transitions with Metadata Preservation

How-To-Articles
1

Implementing Bulk Recording Migration Tools for Platform Transitions with Metadata Preservation

What This Guide Covers

This guide details the architecture and implementation of a data pipeline that migrates voice recording files and their associated interaction metadata from a legacy CCaaS platform to a new target environment. You will build a service that extracts raw media and structured JSON metadata via source APIs, transforms the data to match the target schema, and ingests the assets using the target platform’s ingestion endpoints while preserving searchability and compliance retention tags.

Prerequisites, Roles & Licensing

Licensing Requirements

  • Source Platform: Requires an account with Admin > Analytics > Read or equivalent permissions to access historical interaction data.
  • Target Platform (Genesys Cloud CX): Requires a CX 2 license or higher for agents and supervisors to access recordings. The service account performing the migration must have Architect or Admin privileges.
  • Storage Tier: Ensure the target environment has sufficient storage capacity allocated for the raw audio files. Genesys Cloud retains recordings based on the configured retention policy; migrating large volumes may require negotiating storage limits with your account team.

Permissions & OAuth Scopes

The migration service must operate under a Service Account with the following OAuth scopes configured in the Developer Console:

Target Platform (Genesys Cloud) Ingestion Scopes:

  • analytics:call:read (To verify metadata structure)
  • interaction:create (To create the interaction record if using the Interaction API)
  • recording:upload (If using a custom upload endpoint, though standard API ingestion is preferred)
  • user:read (To map legacy agent IDs to new Genesys Cloud User IDs)
  • routing:queue:read (To map legacy queues to new Genesys Cloud Routing Queues)

Source Platform Extraction Scopes:

  • read:interactions
  • read:recordings
  • read:users
  • read:queues

External Dependencies

  • Object Storage Bucket (S3/Azure Blob): A staging area to hold raw audio files during transformation. This is critical because direct streaming from Source API to Target API often fails due to timeout limits or transient network errors.
  • Mapping Table: A persistent database (PostgreSQL/Redis) to store the correlation between Source Entity IDs (e.g., Legacy Agent ID AGT-992) and Target Entity IDs (e.g., Genesys Cloud User ID 550e8400-e29b-41d4-a716-446655440000).

The Implementation Deep-Dive

1. Metadata Extraction and Schema Normalization

The most common failure in migration projects is treating recordings as simple files. In a CCaaS context, a recording is merely the media payload for a rich interaction object. If you migrate the MP3 but lose the metadata, you render the recording useless for Quality Management (WEM) and Speech Analytics.

The Architectural Reasoning

You must extract the following metadata fields from the source platform:

  • Timestamps: Start time, end time, wait time, talk time.
  • Participants: Agent ID, Customer Number (masked or hashed if PII), Direction (Inbound/Outbound).
  • Routing Context: Queue ID, Skill Group, IVR Node ID.
  • Disposition: Outcome code, wrap-up code.

The Trap: Directly mapping legacy IDs to new IDs during the extraction phase.
If your source system uses integer IDs (e.g., 1024) and your target system uses UUIDs, attempting to resolve the target UUID during the extraction loop causes massive latency. The extraction service must remain stateless regarding the target platform. Extract raw legacy data, save it to your staging database, and perform ID resolution in a separate transformation job.

Implementation Steps

  1. Define the Canonical Metadata Schema.
    Create a JSON schema that serves as the intermediate format. This schema must be superset-compatible with both source and target requirements.

    {
  "legacy_interaction_id": "INT",
  "legacy_start_time": "ISO8601",
  "legacy_end_time": "ISO8601",
  "direction": "INBOUND|OUTBOUND",
  "agent_legacy_id": "STRING",
  "customer_phone": "STRING",
  "queue_legacy_id": "STRING",
  "disposition_code": "STRING",
  "recording_url_source": "URL",
  "recording_format": "MP3|WAV",
  "duration_seconds": "INT"
}

  2. Implement the Extraction Service.
    Use a cursor-based pagination approach to avoid LIMIT/OFFSET performance degradation on large datasets.

    Python Example (Source Extraction):

    import requests
import json

def fetch_interactions(source_api_base, api_token, cursor=None):
    headers = {
        "Authorization": f"Bearer {api_token}",
        "Content-Type": "application/json"
    }
    params = {
        "view": "default",
        "size": 1000
    }
    if cursor:
        params["cursor"] = cursor

    response = requests.get(f"{source_api_base}/api/v2/analytics/interactions/query", headers=headers, params=params)
    response.raise_for_status()
    data = response.json()

    interactions = data.get("entities", [])
    next_cursor = data.get("metadata", {}).get("pagination", {}).get("nextPageCursor")

    return interactions, next_cursor

  3. Persist Raw Data.
    Store the extracted JSON and download the raw audio file to your staging S3 bucket. Preserve the original filename or generate a deterministic hash-based filename to prevent collisions.

    # Example S3 upload command for staging
aws s3 cp /tmp/recording_12345.mp3 s3://migration-staging-bucket/recordings/12345.mp3 --acl private

2. ID Mapping and Entity Resolution

Before ingesting into the target platform, you must resolve every legacy ID to a valid Genesys Cloud UUID. This is the most computationally expensive step if done incorrectly.

The Architectural Reasoning

Genesys Cloud APIs are strict. If you attempt to create a recording interaction with an invalid userId or queueId, the API returns a 400 Bad Request. You cannot “create” a user or queue on the fly during migration without proper provisioning.

The Trap: Assuming phone numbers are unique identifiers for agents.
Legacy systems often allow multiple agents to share an extension or use generic outbound numbers. You must map by Legacy Agent ID to Genesys Cloud User ID using a pre-generated mapping file provided by the implementation team. Do not rely on runtime lookups via phone number.

Implementation Steps

  1. Load Mapping Tables.
    Ingest CSV mapping files into your migration service’s local cache or database.

    legacy_agent_id,genesys_user_id,genesys_user_name
AGT-001,550e8400-e29b-41d4-a716-446655440001,John Doe
AGT-002,550e8400-e29b-41d4-a716-446655440002,Jane Smith

  2. Resolve Queues and Skills.
    Create a similar map for legacy_queue_id to genesys_queue_id. If a legacy queue does not exist in the target, map it to a default “Unmapped” queue or skip the record based on business rules.

  3. Transform Metadata.
    Convert the canonical schema into the target payload. For Genesys Cloud, the most robust method for historical data ingestion is using the Interaction API to create an interaction object with a recording segment, or using the Archive API if available for bulk historical loads. Note: Standard Genesys Cloud does not have a “bulk upload recording” API for live agents; you often must simulate the interaction creation or use a partner tool. However, for pure archival purposes, you may use the File Upload API if your license supports custom object storage, but the standard approach for searchable recordings is to ensure the interaction exists in Genesys Cloud.

    Note: If you are migrating to Genesys Cloud and need the recordings to appear in the “Recordings” tab, you typically cannot simply upload an MP3. You must create the Interaction record first. If the interaction was not handled in Genesys Cloud, you must use the Interaction API to create a synthetic interaction.

    Genesys Cloud Interaction Payload Construction:

    {
  "id": "new-uuid-generated-here",
  "type": "call",
  "direction": "inbound",
  "createdTime": "2023-10-27T10:00:00.000Z",
  "updatedTime": "2023-10-27T10:05:00.000Z",
  "participants": [
    {
      "id": "550e8400-e29b-41d4-a716-446655440001",
      "type": "agent",
      "wrapUpCode": "Closed"
    },
    {
      "id": "external-uuid",
      "type": "customer",
      "phone": "+15550199"
    }
  ],
  "routing": {
    "queue": {
      "id": "queue-uuid-here",
      "name": "Support Queue"
    }
  },
  "segments": [
    {
      "id": "segment-uuid",
      "type": "call",
      "start": "2023-10-27T10:00:00.000Z",
      "end": "2023-10-27T10:05:00.000Z",
      "recording": {
        "id": "recording-uuid",
        "type": "call",
        "status": "complete",
        "url": "https://s3-us-west-2.amazonaws.com/migration-bucket/12345.mp3",
        "duration": 300000
      }
    }
  ]
}

    Critical Note: Genesys Cloud does not natively fetch recordings from external URLs for display in the UI unless you use a custom integration or the recording is stored in Genesys Cloud’s internal storage. For a true migration where recordings must be playable in the Genesys Cloud UI, you must upload the binary file to Genesys Cloud’s storage. This is often done via the File Upload API (/api/v2/files) and then linking the file reference to the interaction.

3. Binary Ingestion and Linking

Uploading large audio files requires handling multipart uploads, retries, and rate limits.

The Architectural Reasoning

Genesys Cloud’s POST /api/v2/files endpoint expects a multipart/form-data request. You must generate a unique file name and upload the binary. Once uploaded, you receive a fileId. You then update the interaction or create a recording object that references this fileId.

The Trap: Ignoring Rate Limits and Concurrency.
Genesys Cloud APIs have rate limits (e.g., 100 requests per second for file uploads). If you spawn 500 threads uploading simultaneously, you will hit 429 Too Many Requests and corrupt your state. You must implement a Token Bucket Algorithm or use an async queue with a concurrency limit of 10-20 workers.

Implementation Steps

  1. Upload Binary File.
    Use a library like requests-toolbelt in Python to handle multipart uploads without loading the entire file into memory.

    import requests
from requests_toolbelt.multipart.encoder import MultipartEncoder

def upload_recording_file(access_token, file_path, file_name):
    url = "https://api.mypurecloud.com/api/v2/files"
    headers = {
        "Authorization": f"Bearer {access_token}",
        "Accept": "application/json"
    }

    # Prepare multipart form data
    m = MultipartEncoder(
        fields={
            'file': (file_name, open(file_path, 'rb'), 'audio/mpeg'),
            'name': file_name,
            'overwrite': 'true'
        }
    )
    headers['Content-Type'] = m.content_type

    response = requests.post(url, headers=headers, data=m)
    response.raise_for_status()
    return response.json()

  2. Link File to Interaction.
    After receiving the id (fileId) from the upload, you must associate it with the interaction created in Step 2. Depending on the API version and specific migration tooling, this may involve updating the interaction’s recording segment or creating a separate recording object.

    Note: If you are using the Genesys Cloud “Historical Data Import” feature (if available in your specific partner tooling), the process may abstract this. However, for native API migration, the sequence is: Upload File → Get File ID → Create/Update Interaction with File ID.

  3. Apply Retention Policies.
    Ensure the uploaded files are tagged with the correct retention policy. If the source recording was marked for “7-Year Retention” (e.g., financial compliance), you must apply a corresponding Tag or Custom Attribute in Genesys Cloud that triggers the appropriate retention rule.

Validation, Edge Cases & Troubleshooting

Edge Case 1: Clock Skew and Timestamp Precision

The Failure Condition:
Recordings appear in the wrong date range in the Genesys Cloud UI, or analytics reports show negative durations.

The Root Cause:
The source system uses Unix timestamps (milliseconds) while Genesys Cloud requires ISO 8601 strings with UTC timezone indicators (Z). Additionally, if the source system has clock drift, the start time might be after the end time if not normalized.

The Solution:
In your transformation layer, enforce strict UTC conversion.

from datetime import datetime, timezone

def normalize_timestamp(unix_ts_ms):
    # Convert milliseconds to datetime object in UTC
    dt = datetime.fromtimestamp(unix_ts_ms / 1000.0, tz=timezone.utc)
    # Format to ISO 8601
    return dt.isoformat()

Always validate that start_time < end_time. If the source data has end_time < start_time, swap them and log a warning.

Edge Case 2: Missing Agent Mappings

The Failure Condition:
The migration script fails with a 404 Not Found or 400 Bad Request when creating the interaction, citing an invalid userId.

The Root Cause:
The legacy agent no longer exists in the target system (e.g., they left the company), or the mapping file is incomplete.

The Solution:
Implement a “Fallback User” strategy. Create a dedicated Genesys Cloud User named “Historical Migration - Unknown Agent” and assign all unmapped recordings to this user. This preserves the recording data and allows QA teams to review the content, even if the specific agent attribution is lost.

{
  "participants": [
    {
      "id": "fallback-unknown-user-uuid",
      "type": "agent",
      "name": "Unknown Agent (Migration)"
    }
  ]
}

Edge Case 3: Audio Format Incompatibility

The Failure Condition:
The file uploads successfully, but the Genesys Cloud UI fails to play the recording, showing a “Media Error.”

The Root Cause:
The source system exported recordings in a format not supported by Genesys Cloud’s web player (e.g., proprietary .wav variants, .amr, or .aac with specific codecs). Genesys Cloud primarily supports .mp3 and .wav (PCM).

The Solution:
Implement a media transcoding step in your pipeline using ffmpeg before uploading.

# Transcode to standard MP3
ffmpeg -i input.amr -codec:a libmp3lame -q:a 2 output.mp3

Add a validation step that checks the MIME type and codec of the output file before attempting the API upload.

Edge Case 4: PII Masking Discrepancies

The Failure Condition:
Customer phone numbers or SSNs are audible in the migrated recordings, violating PCI-DSS or HIPAA compliance, even though the source platform masked them in the UI.

The Root Cause:
Many CCaaS platforms mask PII in the UI but store the raw, unmasked audio on disk. If you migrate the raw file, you bypass the UI-level masking.

The Solution:
Verify with your security team whether the source recordings are raw or masked. If they are raw, you must either:

  1. Use Genesys Cloud’s Speech Analytics or Data Masking features post-migration to redact PII.
  2. Apply audio watermarking or noise injection to PII segments during the transcoding phase (complex and requires NLP detection).
  3. Store the recordings in a restricted, encrypted bucket and only expose them to authorized users via a custom portal, rather than the standard Genesys Cloud UI.

Official References