Azure Blob Connector Setup Guide

Back to Connectors Set up the Azure Blob Storage connector to import conversation recordings, transcripts, and metadata from Azure Blob Storage into Quality AI Express. The Azure Blob Storage Connector imports conversation recordings, transcripts, and metadata from Microsoft Azure Blob Storage into Quality AI Express on a configurable schedule. Use this connector to analyze interactions from third-party CCaaS platforms.

Section	Description
Prerequisites	Azure storage account requirements and platform setup.
Authentication Method	How to authenticate the connector using an Azure Storage connection string.
Supported Data Types	Voice and chat formats and file structure options.
Set Up the Connector	Steps to create, test, map queues, and schedule the connector.
Troubleshooting	Fixes for authentication failures, storage access errors, and data processing issues.

Prerequisites

Azure Storage Requirements

Requirement	Description
Storage Account	Azure Storage account with Blob access subscription.
Authentication	Connection string.
Container	Azure Blob container with conversation files.
Supported File Types	WAV, MP3, JSON, CSV.
Maximum File Size	50 MB per file.
Timestamp Format	ISO 8601 UTC (`YYYY-MM-DDTHH:MM:SSZ`).

Platform Requirements

Requirement	Description
Quality AI Express	Connector feature enabled.
Agent Setup	Agents configured with valid email addresses.
Queue Setup	Quality AI queues configured and available for mapping.
Permissions	Access to Connectors configuration.

Authentication Method

The connector authenticates with Azure Blob Storage using a connection string.

Connection String

Open the Azure portal.
Navigate to Storage Account.
Go to Security + Networking > Access Keys.
Copy the Connection String for Key1 or Key2, and use it to configure the connector.

The fields displayed in the connector vary depending on the selected Auth Type.

Supported Data Types

Data Type	Format	Files per Conversation	Analytics	Notes
Stereo Voice	WAV, MP3	1	Complete Analytics, Enhanced Analytics	Single dual-channel recording (agent + customer).
Mono Voice	WAV, MP3	2	Complete Analytics, Enhanced Analytics	Separate agent and customer recordings. Mixed audio isn’t supported.
Voice Transcript	JSON	1	Text Analytics, Complete Text Analytics	Pre-transcribed voice conversation.
Chat Transcript	JSON	1	Text Analytics, Complete Text Analytics	Message-level chat transcript.

Storage Structure Options

Choose a folder structure for your Azure Blob container before setting up the connector. Option A: Unified Path

container/
├── metadata.csv
├── audio/
│   ├── conv-123456.wav
│   ├── conv-123457-agent.wav
│   └── conv-123457-customer.wav
├── chat/
│   └── chat-123459.json
└── test.csv

Option B: Separate Voice and Chat Paths

container/
├── voice/
│   ├── metadata.csv
│   ├── recordings/
│   └── test.csv
└── chat/
    ├── metadata.csv
    ├── transcripts/
    └── test.csv

Set Up the Connector

Before you begin, verify that your Blob container is ready:

Upload all required files to Azure Blob Storage.
Metadata .csv files contain required fields and valid values.
Mono conversations have separate agent and customer files.
The container and folder paths are available.
A test.csv file exists in each ingestion path.
Blob permissions enable the connector to access the files.

File and folder names shouldn’t contain any spaces or special characters.

Step 1: Create the Connector

Navigate to Quality AI > Configure > Connectors.
Select + Add Connector > Azure Blob Storage > Connect.
Enter a Name for the connector.
Select the Azure Region for the storage account.
Set Authentication Type to Connection String.
Enter the Connection String from your Azure Storage account.
Configure the storage paths:
- Unified Path: Enter a single Blob path for both voice and chat data.
- Separate Paths: Enter a Voice Path and a Chat Path.

Step 2: Test the Connection

Select the Test tab.
Select Test to run the validation checks.
Verify that the following checks pass: Authentication, File Path Access, File Format Validation, and Metadata Validation.

Step 3: Map Queues

Select the Queue tab.
Map each queueId from the .csv files to a Quality AI Express queue. Values must match exactly.
Select Save.

Every queue ID in the metadata file must map to an existing Quality AI Express queue before ingestion can run. Evaluation forms linked to mapped queues determine how interactions are evaluated.

Step 4: Configure the Schedule

Select the Schedule tab.
Set the Interval Type, Start Date, Repeat Every, and Start Time (UTC).
Select Save to activate the schedule.

After saving, verify the setup is complete:

Verify that the system saved and validated the queue mappings.
Verify that the processing schedule is active.
Verify that the first ingestion job appears in the Log tab.
Verify that the system reports no processing errors.

The setup is complete when all conversations appear in Quality AI Express dashboards and analytics data is available for ingested interactions.

Step 5: Monitor Ingestion Jobs

Use the Log tab to monitor ingestion activity.

Column	Description
Start Time	Job start time.
Finish Time	Job completion time.
Uploaded	Number of uploaded files.
Processing	Number of processed files.
Job Status	Current processing status.
View Details	Opens job details.

Ingestion is successful when the uploaded and processed file counts match, the job shows as completed, and no errors appear in the job details.

Troubleshooting

Authentication Issues

Problem	Symptom	Resolution
Authentication Failed	Authentication error.	Verify the account key, connection string, and storage account name. Re-copy the credentials from the Azure portal and test again.
Authorization Header Error	Request rejected.	Generate a new access key and update the connector configuration.

Storage Access Issues

Problem	Symptom	Resolution
Container Not Found	Container or path not found.	Verify the container name and folder structure.
File Not Available	Blob access denied.	Verify blob access permissions and storage access settings.

File Validation Issues

Problem	Symptom	Resolution
File Format Validation Error	Unsupported or invalid file format.	Verify the CSV metadata structure and required headers.
Metadata Validation Error	Missing or invalid metadata.	Verify required metadata fields and timestamp values.

Data Processing Issues

Problem	Symptom	Resolution
Timestamp Validation Error	Invalid timestamp format.	Use ISO 8601 UTC format (`YYYY-MM-DDTHH:MM:SSZ`).
Queue Mapping Error	Queue IDs not mapped.	Map all queue IDs before running ingestion.

Performance Considerations

Processing time depends on file size, audio duration, transcription latency, and AI analysis complexity.

​Prerequisites

​Azure Storage Requirements

​Platform Requirements

​Authentication Method

​Connection String

​Supported Data Types

​Supported Data Types

​Storage Structure Options

​Set Up the Connector

​Step 1: Create the Connector

​Step 2: Test the Connection

​Step 3: Map Queues

​Step 4: Configure the Schedule

​Step 5: Monitor Ingestion Jobs

​Troubleshooting

​Authentication Issues

​Storage Access Issues

​File Validation Issues

​Data Processing Issues

​Performance Considerations

Prerequisites

Azure Storage Requirements

Platform Requirements

Authentication Method

Connection String

Supported Data Types

Supported Data Types

Storage Structure Options

Set Up the Connector

Step 1: Create the Connector

Step 2: Test the Connection

Step 3: Map Queues

Step 4: Configure the Schedule

Step 5: Monitor Ingestion Jobs

Troubleshooting

Authentication Issues

Storage Access Issues

File Validation Issues

Data Processing Issues

Performance Considerations