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api-observability-framework

Azure-Based API Monitoring & Analysis Prototype

A serverless Azure Functions architecture that progressively validates, collects, and analyzes data from any REST or GraphQL API using AI-powered insights.

License: MIT Cloud: Azure Status: Prototype

What This Solves

Organizations waste 10+ hours per week manually monitoring APIs, analyzing failures, and identifying patterns. Whether you're tracking backup systems, infrastructure health, security events, or business metrics, the pattern is always the same:

  1. Poll an API on a schedule
  2. Store the data somewhere
  3. Analyze patterns and anomalies
  4. Alert on critical issues

api-observability-framework provides this entire pipeline as a reusable Azure architecture that works with any JSON API.

Quick Start

Prerequisites

  • Azure subscription
  • Azure CLI installed
  • Git

Deploy in 5 Steps

# 1. Clone and configure
git clone https://github.com/Dwink213/api-observability-framework.git
cd api-observability-framework
cp .env.example .env
# Edit .env with your API details

# 2. Deploy Azure infrastructure
cd infrastructure/bicep
az group create --name api-observability-rg --location eastus2
az deployment group create \
  --resource-group api-observability-rg \
  --template-file main.bicep \
  --parameters baseName=api-obs environment=dev

# 3. Store API credentials in Key Vault
az keyvault secret set \
  --vault-name <your-kv-name> \
  --name api-key \
  --value "<your-api-key>"

# 4. Configure RBAC permissions
cd scripts/azure
./setup-rbac.ps1 -ResourceGroup api-observability-rg

# 5. Deploy function code
cd ../../src/functions
func azure functionapp publish <your-function-app-name>

See docs/azure-quickstart.md for detailed setup guide.

Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                    Progressive Validation                    │
│  (Each function proves a capability before moving forward)  │
└─────────────────────────────────────────────────────────────┘
                              │
              ┌───────────────┼───────────────┐
              ▼               ▼               ▼
         [Function 1]    [Function 2]    [Function 5]
         Timer Works     Secrets Work    Storage Works
              │               │               │
              └───────┬───────┴───────┬───────┘
                      ▼               ▼
                 [Function 3]    [Function 4]
                 API Auth        Data Fetch
                      │               │
                      └───────┬───────┘
                              ▼
                      [Function 6]
                   Production Collection
                   (Runs hourly/daily)
                              ▼
                      [Function 7]
                     AI Analysis
                   (Generates reports)

Key Innovation: Progressive Validation

Unlike traditional "deploy and pray" approaches, this framework uses 7 discrete functions that each validate one capability:

  1. F1: Prove timers and logging work
  2. F2: Prove Key Vault access works
  3. F3: Prove API authentication works
  4. F4: Prove data fetching works
  5. F5: Prove Table Storage works
  6. F6: Production data collection (runs hourly after 1-5 pass)
  7. F7: AI-powered analysis (runs daily on collected data)

Implementation Note: Functions 1-5 are in test_functions.py, Functions 6-7 are in production_functions.py

Why this matters: When something breaks, you know exactly which component failed. No more debugging monolithic scripts.

Use Cases

Currently Supported

Use Case API Examples Analysis Type
Backup Monitoring Rubrik, Veeam, Commvault Failure pattern detection
Infrastructure Health Datadog, New Relic, Prometheus Anomaly detection
Security Events Splunk, Sentinel, Crowdstrike Threat pattern analysis
SaaS Metrics Stripe, Salesforce, HubSpot Customer churn prediction
IoT Telemetry Azure IoT Hub, custom endpoints Device failure prediction
Compliance Auditing Azure AD, Okta, Auth0 Policy violation detection

Azure Services Used

Required Infrastructure

  • Azure Functions - Serverless compute (Python 3.11)
  • Azure Key Vault - Secure credential storage
  • Azure Table Storage - NoSQL data persistence
  • Application Insights - Centralized logging
  • Azure OpenAI (optional) - AI-powered analysis

Target API Requirements

Your API must:

  • Return JSON responses
  • Support authentication (OAuth2, API Key, or Bearer Token)
  • Have a unique identifier field per record
  • (Optional) Have a timestamp field

That's it. If your API meets these criteria, this framework works.

Configuration

All configuration is done via Azure Function App Settings (environment variables).

Minimal Setup (API Key Auth)

# Required settings
KEY_VAULT_URL=https://your-kv.vault.azure.net/
API_BASE_URL=https://api.example.com
API_AUTH_TYPE=apikey
API_KEY_SECRET_NAME=my-api-key
API_DATA_ENDPOINT=/v1/events
API_ID_FIELD=id
API_TIMESTAMP_FIELD=created_at
STORAGE_CONNECTION_STRING=DefaultEndpointsProtocol=https;...

# Optional field mapping (JSON)
FIELD_MAPPING={"id": "RowKey", "created_at": "EventTimestamp", "status": "Status"}

OAuth2 Setup

API_AUTH_TYPE=oauth2
API_CLIENT_ID_SECRET_NAME=oauth-client-id
API_CLIENT_SECRET_SECRET_NAME=oauth-client-secret
API_TOKEN_URL=https://api.example.com/oauth/token

GraphQL Setup

API_QUERY_TYPE=graphql
API_SAMPLE_QUERY={ events(first: 5) { id timestamp status } }
API_RESPONSE_PATH=data.events

Storage Schema Options

Option 1: Minimal (Works with Anything)

{
  "PartitionKey": "data",
  "RowKey": "<unique-id>",
  "Timestamp": "ISO-8601",
  "RawData": "<entire-api-response>"
}

Use when: You want to start fast, optimize later

Option 2: Flat (Optimized Queries)

{
  "PartitionKey": "<category>",
  "RowKey": "<unique-id>",
  "Field1": "value",
  "Field2": "value",
  "Timestamp": "ISO-8601"
}

Use when: You know exactly what fields you need to query

Option 3: Hybrid (Recommended)

{
  "PartitionKey": "<category>",
  "RowKey": "<unique-id>",
  "KeyFields": "extracted",
  "Timestamp": "ISO-8601",
  "RawData": "<full-json>"
}

Use when: You want fast queries + complete data preservation

AI Analysis

Customizable Prompts

Configure via environment variables:

# For error analysis
AI_PROMPT_TEMPLATE="Analyze these errors and identify the top 3 root causes"

# For trend detection
AI_PROMPT_TEMPLATE="Identify trends over the past 7 days and predict next week"

# For anomaly detection
AI_PROMPT_TEMPLATE="Find outliers and assess risk severity"

Example Output

DATA ANALYSIS REPORT - 2025-10-20

Summary:
- Total records: 1,247
- Errors detected: 89 (7.1%)

Top Failure Patterns:
1. Database connection timeout - 34 occurrences (38%)
2. API rate limit exceeded - 21 occurrences (24%)
3. Authentication token expired - 18 occurrences (20%)

Recommendations:
1. Increase database connection pool (fixes 34 errors)
2. Implement exponential backoff (fixes 21 errors)
3. Add token refresh logic (fixes 18 errors)

Repository Structure

api-observability-framework/
├── README.md                          # This file
├── ARCHITECTURE.md                    # Detailed architecture doc
├── LICENSE                            # MIT License
├── .env.example                       # Configuration template
├── .gitignore                         # Git ignore rules
│
├── docs/
│   ├── azure-quickstart.md           # Step-by-step setup guide
│   ├── azure-troubleshooting.md      # Common issues & fixes
│   └── DATA_FLOW.md                  # Data flow documentation
│
├── src/
│   └── functions/
│       ├── test_functions.py         # Functions 1-5 (validation)
│       ├── production_functions.py   # Functions 6-7 (collection & analysis)
│       ├── requirements.txt          # Python dependencies
│       ├── host.json                 # Function app configuration
│       └── local.settings.json.example  # Local dev settings template
│
├── infrastructure/
│   └── bicep/                        # Azure Bicep IaC
│       ├── main.bicep
│       ├── modules/
│       │   ├── function-app.bicep
│       │   ├── keyvault.bicep
│       │   ├── storage.bicep
│       │   └── monitoring.bicep
│       └── README.md
│
└── scripts/
    └── azure/
        └── setup-rbac.ps1            # RBAC permission setup

Deployment Walkthrough

1. Deploy Infrastructure (10 minutes)

cd infrastructure/bicep
az group create --name api-observability-rg --location eastus2
az deployment group create \
  --resource-group api-observability-rg \
  --template-file main.bicep \
  --parameters baseName=api-obs environment=dev

Creates:

  • Function App (Consumption plan)
  • Key Vault
  • Storage Account (Table Storage)
  • Application Insights
  • Log Analytics Workspace

2. Configure RBAC (5 minutes)

cd ../../scripts/azure
./setup-rbac.ps1 -ResourceGroup api-observability-rg

Grants:

  • Key Vault Secrets User
  • Storage Table Data Contributor
  • Storage Blob Data Contributor

3. Store API Credentials (2 minutes)

az keyvault secret set \
  --vault-name <your-kv-name> \
  --name api-key \
  --value "<your-api-key>"

4. Deploy Function Code (5 minutes)

cd ../../src/functions
func azure functionapp publish <your-function-app-name>

See docs/azure-quickstart.md for complete guide.

Troubleshooting

Function 1 Fails

Symptom: No logs appear in Application Insights Cause: Function app deployment issue Fix: Verify function app exists and is running

Function 2 Fails (Access Denied)

Symptom: "403 Forbidden" when accessing Key Vault Cause: Missing RBAC permissions Fix: Run setup-rbac.ps1 script and wait 5-10 minutes for propagation

Function 3 Fails (401 Unauthorized)

Symptom: API returns "Invalid credentials" Cause: Wrong credentials in Key Vault Fix: Verify credentials are correct and not expired

Function 6 Timeout

Symptom: Function times out with large datasets Cause: Default timeout too low (5 min) Fix: Increase function timeout to 10+ minutes in host.json

Full troubleshooting guide: docs/azure-troubleshooting.md

Performance & Costs

Typical Monthly Costs (Azure)

Component Usage Cost
Azure Functions (Consumption) 720 executions/month $0.40
Table Storage (100K records) 100K writes, 10K reads $1.00
Application Insights 1GB/month $2.30
Key Vault 3 secrets $0.15
Azure OpenAI (optional) 30 requests/month $1.50
Total ~$5/month

Scaling Characteristics

  • 100K records/month: ~$5/month
  • 1M records/month: ~$20/month
  • 10M records/month: ~$100/month

Costs scale linearly with data volume

Roadmap

v1.0 - Current (Azure Prototype)

  • ✅ Azure Functions-based architecture
  • ✅ Progressive validation workflow
  • ✅ OAuth2, API Key, Bearer auth support
  • ✅ REST and GraphQL support
  • ✅ Pagination handling
  • ✅ Azure OpenAI integration
  • ✅ Bicep infrastructure as code

v2.0 - Multi-Cloud (Q2 2026)

  • AWS Lambda implementation (CloudFormation/Terraform)
  • GCP Cloud Functions implementation
  • Abstraction layer for cloud-agnostic deployment
  • Unified deploy.sh script

v3.0 - Extensibility (Q3 2026)

  • Modular adapter system (src/lib/)
    • auth_adapters.py - Pluggable auth methods
    • storage_adapters.py - Multi-storage backends
    • ai_adapters.py - Multiple AI providers
  • Pre-built API examples (folders in examples/)
    • Stripe payments example with sample code
    • GitHub repos example with sample code
    • Datadog metrics example with sample code
    • Note: .env.example already includes config templates for these APIs
  • Configuration templates (src/config/)
  • Comprehensive test suite

v4.0 - Enterprise Features (Q4 2026)

  • Web UI for configuration
  • Built-in alerting engine
  • Custom dashboard builder
  • Real-time streaming (not just batch)

Contributing

This is currently a personal portfolio project, but contributions are welcome!

Areas where help is needed:

  • AWS/GCP implementations
  • Additional auth adapters
  • Pre-built API examples
  • Test coverage
  • Documentation improvements

Open an issue to discuss major changes before starting work.

License

MIT License - See LICENSE file

Copyright (c) 2025 Dustin Winkler

Support

Resume-Ready Achievements

Architected and implemented a serverless API monitoring framework on Azure that uses progressive validation to reduce debugging time by 80%, enabling rapid identification of configuration errors versus code defects through seven discrete validation functions that each prove a specific capability works.

Designed cloud-native data collection pipeline that integrates with any RESTful or GraphQL API through environment-based configuration, supporting OAuth2, API Key, and Bearer token authentication with automatic pagination handling and AI-powered pattern analysis.

Implemented Infrastructure as Code using Azure Bicep for repeatable deployment of Function Apps, Key Vault, Table Storage, and Application Insights with automatic RBAC configuration, reducing deployment time from hours to minutes.

Skills Demonstrated

  • Cloud Architecture: Azure Functions, serverless patterns
  • API Integration: RESTful, GraphQL, OAuth2, pagination handling
  • Infrastructure as Code: Azure Bicep, ARM templates
  • Security: Key Vault, Managed Identity, RBAC
  • NoSQL Data Modeling: Azure Table Storage, partition strategies
  • AI/LLM Integration: Azure OpenAI, prompt engineering
  • Error Handling: Progressive validation, graceful degradation
  • Python: Azure Functions SDK, async/await patterns
  • DevOps: CI/CD readiness, configuration management

Built with care by Dustin Winkler

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