Building a Daily Genesys Cloud Analytics Export Job to AWS S3 with Python

What You Will Build

• A Python script that queries Genesys Cloud Conversation Details Analytics, paginates through all results, and streams the data to an AWS S3 bucket.
• This uses the Genesys Cloud PureCloud Platform Client V2 SDK and the AWS Boto3 SDK.
• The language is Python 3.9+.

Prerequisites

• Genesys Cloud OAuth Application: You need a client_credentials type OAuth app.
• Required Scopes: analytics:conversation:view is mandatory for reading conversation data. If you need specific attributes like queue or user, ensure the app has access to those resources, though the analytics scope usually suffices for the aggregated view.
• AWS Account: An active AWS account with a target S3 bucket created.
• IAM Permissions: An IAM user or role with s3:PutObject permissions on the target bucket.
• SDK Versions:
  • genesys-cloud-purecloud-platform-client (v2.2.0+)
  • boto3 (v1.28.0+)
  • pandas (v2.0.0+) for efficient DataFrame handling and JSON serialization.
  • requests (for fallback OAuth if SDK auth fails, though SDK is preferred).
• Environment Variables:
  • GENESYS_CLIENT_ID
  • GENESYS_CLIENT_SECRET
  • GENESYS_REGION (e.g., us-east-1, eu-west-1)
  • AWS_ACCESS_KEY_ID
  • AWS_SECRET_ACCESS_KEY
  • AWS_REGION
  • S3_BUCKET_NAME

Authentication Setup

Genesys Cloud APIs use OAuth 2.0. The SDK handles the token exchange and refresh automatically when initialized correctly. You must configure the Configuration object with your region and credentials.

import os
from purecloudplatformclientv2 import Configuration, ApiClient, AnalyticsApi
from purecloudplatformclientv2.rest import ApiException

def get_genesys_api_client() -> AnalyticsApi:
    """
    Initializes and returns a configured Genesys Cloud Analytics API client.
    """
    # Load credentials from environment variables
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    region = os.getenv("GENESYS_REGION", "us-east-1")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set.")

    # Configure the SDK
    config = Configuration()
    config.host = f"https://api.{region}.mypurecloud.com"
    config.access_token = None  # SDK will handle this
    
    # Set the client credentials for the SDK to use internally
    # Note: The SDK typically uses an environment variable or explicit setting for OAuth flow.
    # In newer SDK versions, you often set the client_id/secret in the config or use a specific auth helper.
    # Here we use the standard method for client_credentials flow.
    config.client_id = client_id
    config.client_secret = client_secret
    
    api_client = ApiClient(configuration=config)
    analytics_api = AnalyticsApi(api_client)
    
    return analytics_api

Note on OAuth Scopes: The analytics:conversation:view scope is required for the post analytics conversations details query endpoint. If your OAuth app does not have this scope, the API will return a 403 Forbidden error.

Implementation

Step 1: Define the Analytics Query Payload

The Genesys Cloud Analytics API uses a specific JSON structure to define what data to retrieve. You must specify the interval, view (e.g., conversation), and the groupBy or select attributes you wish to export.

For a daily export, you typically query the previous day or a specific date range. The API returns a maximum of 10,000 records per request, so pagination is mandatory.

from datetime import datetime, timedelta
from purecloudplatformclientv2.models import PostConversationDetailsQueryRequest

def build_query_request(start_date: datetime, end_date: datetime) -> PostConversationDetailsQueryRequest:
    """
    Constructs the Genesys Cloud Analytics query request object.
    """
    # Format dates as ISO 8601 strings required by the API
    start_iso = start_date.strftime("%Y-%m-%dT%H:%M:%S.000Z")
    end_iso = end_date.strftime("%Y-%m-%dT%H:%M:%S.000Z")

    # Define the query parameters
    query_params = {
        "interval": f"{start_iso}/{end_iso}",
        "view": "conversation",
        "groupBy": ["user"], # Group by user to get user-level stats
        "select": [
            "user.id",
            "user.name",
            "user.email",
            "queue.id",
            "queue.name",
            "channel",
            "conversationCount",
            "handledCount",
            "talkTime",
            "holdTime",
            "wrapUpTime"
        ]
    }

    # Create the SDK object
    # Note: The SDK model accepts a dict or specific keyword arguments.
    # Using kwargs for clarity in this example.
    request_body = PostConversationDetailsQueryRequest(
        interval=query_params["interval"],
        view=query_params["view"],
        group_by=query_params["groupBy"],
        select=query_params["select"]
    )
    
    return request_body

Step 2: Implement Pagination Logic

The Genesys Cloud Analytics API returns a nextPageToken in the response if more data is available. You must loop until this token is None.

from purecloudplatformclientv2.models import ConversationDetailsQueryResponse

def fetch_all_conversation_details(analytics_api: AnalyticsApi, request_body: PostConversationDetailsQueryRequest) -> list:
    """
    Fetches all paginated results from the Genesys Cloud Analytics API.
    Returns a list of dictionaries representing the data points.
    """
    all_data = []
    next_page_token = None
    page_count = 0

    while True:
        page_count += 1
        try:
            # Add nextPageToken to the request if it exists
            if next_page_token:
                request_body.next_page_token = next_page_token
            
            # Make the API call
            # Method: POST /api/v2/analytics/conversations/details/query
            response: ConversationDetailsQueryResponse = analytics_api.post_analytics_conversations_details_query(
                body=request_body
            )
            
            # Check if there are entities in the response
            if response.entities and len(response.entities) > 0:
                all_data.extend(response.entities)
                print(f"Page {page_count}: Retrieved {len(response.entities)} records.")
            
            # Check for next page
            if response.next_page_token:
                next_page_token = response.next_page_token
            else:
                print("No more pages. Fetching complete.")
                break
                
        except ApiException as e:
            # Handle rate limiting (429) or other errors
            if e.status == 429:
                print("Rate limit hit. Waiting 10 seconds before retrying...")
                import time
                time.sleep(10)
                continue # Retry the same page
            else:
                print(f"API Error: {e.status} - {e.reason}")
                raise

    return all_data

Step 3: Process Data and Upload to S3

Once the data is fetched, you should convert it into a structured format (like a Pandas DataFrame) and then serialize it to a format suitable for S3, such as JSON Lines (jsonl) or CSV. JSON Lines is preferred for analytics data as it preserves nested structures and is easily readable by big data tools like Athena or Spark.

import boto3
import pandas as pd
import io
import json

def upload_to_s3(data: list, bucket_name: str, file_key: str, aws_region: str) -> None:
    """
    Converts the list of data objects to a Pandas DataFrame,
    serializes to JSON Lines, and uploads to S3.
    """
    if not data:
        print("No data to upload.")
        return

    # Convert list of SDK objects to a list of dictionaries
    # The SDK objects have a 'to_dict()' method which is efficient
    dict_data = [item.to_dict() for item in data]
    
    # Create a DataFrame
    df = pd.DataFrame(dict_data)
    
    # Convert to JSON Lines format
    jsonl_buffer = io.StringIO()
    df.to_json(jsonl_buffer, orient='records', lines=True)
    jsonl_content = jsonl_buffer.getvalue()
    jsonl_buffer.close()

    # Initialize S3 Client
    s3_client = boto3.client('s3', region_name=aws_region)

    try:
        # Upload to S3
        s3_client.put_object(
            Bucket=bucket_name,
            Key=file_key,
            Body=jsonl_content.encode('utf-8'),
            ContentType='application/json'
        )
        print(f"Successfully uploaded {len(data)} records to s3://{bucket_name}/{file_key}")
    except Exception as e:
        print(f"Failed to upload to S3: {e}")
        raise

Complete Working Example

Below is the full, runnable Python script. Save this as genesys_s3_export.py.

import os
import sys
import time
import logging
from datetime import datetime, timedelta

# AWS Imports
import boto3

# Genesys Cloud SDK Imports
from purecloudplatformclientv2 import Configuration, ApiClient, AnalyticsApi
from purecloudplatformclientv2.models import PostConversationDetailsQueryRequest
from purecloudplatformclientv2.rest import ApiException

# Data Processing Imports
import pandas as pd
import io

# Configure Logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def get_genesys_api_client() -> AnalyticsApi:
    """Initializes and returns a configured Genesys Cloud Analytics API client."""
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    region = os.getenv("GENESYS_REGION", "us-east-1")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set in environment variables.")

    config = Configuration()
    config.host = f"https://api.{region}.mypurecloud.com"
    config.client_id = client_id
    config.client_secret = client_secret
    
    api_client = ApiClient(configuration=config)
    return AnalyticsApi(api_client)

def build_query_request(start_date: datetime, end_date: datetime) -> PostConversationDetailsQueryRequest:
    """Constructs the Genesys Cloud Analytics query request object."""
    start_iso = start_date.strftime("%Y-%m-%dT%H:%M:%S.000Z")
    end_iso = end_date.strftime("%Y-%m-%dT%H:%M:%S.000Z")

    return PostConversationDetailsQueryRequest(
        interval=f"{start_iso}/{end_iso}",
        view="conversation",
        group_by=["user"],
        select=[
            "user.id", "user.name", "user.email",
            "queue.id", "queue.name",
            "channel",
            "conversationCount", "handledCount",
            "talkTime", "holdTime", "wrapUpTime"
        ]
    )

def fetch_all_conversation_details(analytics_api: AnalyticsApi, request_body: PostConversationDetailsQueryRequest) -> list:
    """Fetches all paginated results from the Genesys Cloud Analytics API."""
    all_data = []
    next_page_token = None
    page_count = 0
    max_retries = 5
    retry_count = 0

    while True:
        page_count += 1
        retry_count = 0 # Reset retry count for new page
        
        while retry_count < max_retries:
            try:
                if next_page_token:
                    request_body.next_page_token = next_page_token
                
                response = analytics_api.post_analytics_conversations_details_query(body=request_body)
                
                if response.entities and len(response.entities) > 0:
                    all_data.extend(response.entities)
                    logger.info(f"Page {page_count}: Retrieved {len(response.entities)} records.")
                
                if response.next_page_token:
                    next_page_token = response.next_page_token
                else:
                    logger.info("No more pages. Fetching complete.")
                    return all_data
                
                break # Break out of retry loop if successful
                
            except ApiException as e:
                if e.status == 429:
                    wait_time = 10 * (retry_count + 1)
                    logger.warning(f"Rate limit hit (429). Waiting {wait_time}s before retrying...")
                    time.sleep(wait_time)
                    retry_count += 1
                else:
                    logger.error(f"API Error: {e.status} - {e.reason}")
                    raise

        if retry_count >= max_retries:
            raise Exception("Max retries exceeded due to rate limiting.")

def upload_to_s3(data: list, bucket_name: str, file_key: str, aws_region: str) -> None:
    """Converts data to JSON Lines and uploads to S3."""
    if not data:
        logger.warning("No data to upload.")
        return

    dict_data = [item.to_dict() for item in data]
    df = pd.DataFrame(dict_data)
    
    jsonl_buffer = io.StringIO()
    df.to_json(jsonl_buffer, orient='records', lines=True)
    jsonl_content = jsonl_buffer.getvalue()
    jsonl_buffer.close()

    s3_client = boto3.client('s3', region_name=aws_region)

    try:
        s3_client.put_object(
            Bucket=bucket_name,
            Key=file_key,
            Body=jsonl_content.encode('utf-8'),
            ContentType='application/json'
        )
        logger.info(f"Successfully uploaded {len(data)} records to s3://{bucket_name}/{file_key}")
    except Exception as e:
        logger.error(f"Failed to upload to S3: {e}")
        raise

def main():
    # 1. Setup Date Range (Example: Previous Day)
    end_date = datetime.utcnow().replace(hour=0, minute=0, second=0, microsecond=0)
    start_date = end_date - timedelta(days=1)
    
    logger.info(f"Exporting data from {start_date} to {end_date}")

    # 2. Initialize Clients
    analytics_api = get_genesys_api_client()
    
    # 3. Build Query
    request_body = build_query_request(start_date, end_date)

    # 4. Fetch Data
    try:
        data = fetch_all_conversation_details(analytics_api, request_body)
    except Exception as e:
        logger.error(f"Failed to fetch data: {e}")
        sys.exit(1)

    # 5. Upload to S3
    s3_bucket = os.getenv("S3_BUCKET_NAME")
    aws_region = os.getenv("AWS_REGION", "us-east-1")
    
    if not s3_bucket:
        logger.error("S3_BUCKET_NAME environment variable is not set.")
        sys.exit(1)

    # Define S3 key with date stamp
    file_key = f"genesys/analytics/conversations/{start_date.strftime('%Y-%m-%d')}.jsonl"

    try:
        upload_to_s3(data, s3_bucket, file_key, aws_region)
    except Exception as e:
        logger.error(f"Export job failed: {e}")
        sys.exit(1)

    logger.info("Daily export job completed successfully.")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token is invalid, expired, or the Client ID/Secret is incorrect.
• Fix: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET in your environment. Ensure the OAuth app is active in the Genesys Cloud Admin Console. The SDK handles token refresh, so if the credentials are valid, this error usually indicates a misconfiguration in the Configuration object.

Error: 403 Forbidden

• Cause: The OAuth app lacks the required scope.
• Fix: Go to Genesys Cloud Admin > Platform > OAuth Applications. Select your app and add the analytics:conversation:view scope. Save the changes. The change may take a few minutes to propagate.

Error: 429 Too Many Requests

• Cause: You have exceeded the Genesys Cloud API rate limits. Analytics endpoints often have lower rate limits than standard CRUD operations.
• Fix: The code above implements a basic exponential backoff retry logic. For high-volume exports, consider spreading the queries over a longer time window or using the Genesys Cloud Reporting API if available for your specific use case, which may have different limits.

Error: Boto3 NoCredentialsError

• Cause: AWS credentials are not found.
• Fix: Ensure AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are set in the environment, or configure AWS CLI credentials via aws configure. If running on EC2, ensure the instance role has S3 permissions.

Error: Pandas DtypeWarning or Data Mismatch

• Cause: Genesys Cloud responses may contain nested objects or varying structures across different conversation types (e.g., Voice vs. Chat).
• Fix: The to_dict() method handles most serialization. If you encounter issues with specific columns, inspect the raw JSON response. You may need to flatten nested dictionaries before creating the DataFrame if you require a strict CSV format. JSON Lines is more resilient to schema variations.

Official References

• Genesys Cloud Analytics API Documentation
• Genesys Cloud Python SDK Reference
• AWS Boto3 S3 Client Documentation
• Pandas JSON Serialization Guide