I'm constantly looking for ways to streamline the process of building connectors. When I needed to create a method for tracking food recalls and safety alerts to mitigate supply chain impact on a customer, I decided to leverage Cursor AI to accelerate the development of a Fivetran Connector SDK solution. To demonstrate the solution, I used the FDA's open API. What followed was an experience that demonstrated how AI can transform the way we build production-ready data pipelines using the Fivetran Connector SDK and AI.

Why Fivetran Connector SDK?

Let’s first explain why the Fivetran Connector SDK is so powerful:

• Standardized framework: Provides a consistent pattern for building connectors
• Built-in Fivetran capabilities: Leverage Fivetran infrastructure to handle state management, error handling, and data transformations in flight
• Production-ready: Designed for enterprise-scale data pipelines
• Flexible: Supports both authenticated and unauthenticated API access, databases, and any datasource you can connect to with Python.

These features and capabilities, designed to radically simplify the process of writing custom connectors, pair extremely well with AI-assisted development.

The AI-assisted development process

We have previously demonstrated how connector SDK and AI assistance can be used to build connectors quickly and cheaply. Compared with Claude, Cursor AI is especially notable for its user-friendly IDE integration and speed. Regardless of the assistant, AI-assisted development involves the following steps:

1. Prompt engineering: The art of being specific

My initial prompt to Cursor AI was comprehensive and specific. I built context files (notes.txt, agent.md, and fields.yaml – see the text of the prompt for more details) and placed them in my project folder to be used in the prompt. I also used the Fivetran Connector SDK System Instructions as a notepad file in Cursor named agents.md. I added this context to the prompt using the @file_name syntax:

Create a Fivetran Connector SDK solution for the FDA Food Enforcement API that includes:
- Incremental syncing using date-based filtering (report_date field)
- Configurable batch processing with pagination support
- Rate limiting and quota management for both authenticated and unauthenticated access
- Automatic JSON flattening for complex nested structures
- Date string normalization to ISO format
- Robust error handling with retry logic
- State management for reliable incremental syncs. Review my @notes.txt for more information.
- Support for both authenticated and unauthenticated API access
- Configurable batch limits for testing and production use
- Follow the best practices outlined in @agent.md
- Infer the data structure from the sample data in @fields.yaml

Being specific about requirements upfront saves significant iteration time later.

2. What Cursor generated: A deep dive

Cursor AI generated a complete connector with several sophisticated features. Let’s break down the key components:

Configuration management

{
   "api_key": "",
   "base_url": "https://api.fda.gov/food/enforcement.json",
   "batch_size": "50",
   "rate_limit_pause": "0.5"
}

The AI correctly identified that the FDA API supports both authenticated and unauthenticated access, with different rate limits for each. To test the solution I opted for the unauthenticated session as it offered plenty of requests/day for development.

Robust error handling

def fetch_data(url: str, params: Dict[str, Any], api_key: Optional[str] = None) -> Dict[str, Any]:
   headers = {}
   if api_key and api_key != "string":
       headers['Authorization'] = f'Basic {api_key}'
       params['api_key'] = api_key
       log.info("Using API key for authentication")
   else:
       log.warning("No API key provided - using default rate limits (240 requests/min, 1,000 requests/day)")
  
   for attempt in range(MAX_RETRIES):
       try:
           response = requests.get(url, params=params, headers=headers)
           response.raise_for_status()
           return response.json()
       except requests.exceptions.RequestException as e:
           if attempt == MAX_RETRIES - 1:
               raise RuntimeError(f"Failed to fetch data after {MAX_RETRIES} attempts: {str(e)}")
           time.sleep(RETRY_DELAY  (attempt + 1))

The AI implemented intelligent retry logic with exponential backoff, a crucial feature for production systems.

Smart data flattening

def flatten_dict(d: Dict[str, Any], parent_key: str = '', sep: str = '_') -> Dict[str, Any]:
   items: List[tuple] = []
   for k, v in d.items():
       new_key = f"{parent_key}{sep}{k}" if parent_key else k
       if isinstance(v, dict):
           items.extend(flatten_dict(v, new_key, sep=sep).items())
       elif isinstance(v, list):
           items.append((new_key, json.dumps(v)))
       else:
           items.append((new_key, v))
   return dict(items)

This function elegantly handles the complex nested JSON structures returned by the FDA API, converting them into tabular format suitable for analytics.

3. AI-generated best practices

What impressed me most was how the AI incorporated several patterns from the agent.md file:

Incremental syncing

 #Add date filter if we have a last sync time
if last_sync_time:
   params["search"] = f"report_date:[{last_sync_time}+TO+{datetime.now().strftime('%Y%m%d')}]"

Rate limiting awareness

 #Adjust rate limit pause based on API key presence
if api_key and api_key != "string":
   rate_limit_pause = float(configuration.get("rate_limit_pause", DEFAULT_RATE_LIMIT_PAUSE))
else:
   rate_limit_pause = NO_API_KEY_RATE_LIMIT_PAUSE
   log.warning(f"Using longer rate limit pause ({rate_limit_pause}s) due to no API key")

State management

# Checkpoint progress
new_state = {
   "skip": skip,
   "last_sync_time": datetime.now(pytz.UTC).strftime("%Y%m%d")
}
yield op.checkpoint(new_state)

The developer experience: What worked and what didn't

Cursor AI handled the following exceptionally well:

1. Complete solution generation: The AI provided a working connector SDK solution in one go, including connector.py, requirements.txt, and configuration.json
2. Best practices integration: Incorporated retry logic, rate limiting, and error handling
3. Documentation: Generated comprehensive README and tutorial files
4. Configuration flexibility: Supported both development and production scenarios

However, the assistant came up short in these areas:

1. Testing strategy: The AI didn't generate unit tests correctly for Fivetran debug
2. Monitoring integration: Could have included more detailed logging for production monitoring
3. Performance optimization: Could have suggested batch size optimization strategies
   

Testing the AI-generated code

Running the connector was straightforward:

#Install dependencies
pip install -r requirements.txt

#Test the connector
fivetran debug --configuration configuration.json

The connector successfully:

• Fetched data from the FDA API
• Flattened complex JSON structures
• Handled pagination correctly
• Implemented proper rate limiting
  

Lessons learned: AI-assisted development best practices

The successes and shortcomings of this experience ultimately boil down into the following best practices for AI-assisted development:

1. Be specific about requirements

The more detailed your initial prompt, the better the output. Include:

• Error handling requirements
• Performance expectations
• Integration patterns
• Security considerations
• Pertinent information about the data source(authentication, pagination, etc.)

2. Review and iterate

While the AI generated excellent code, I still needed to:

• Review the logic for edge cases
• Test with real API responses
• Adjust configuration parameters
• Add custom business logic

3. Understand the generated code

Don't treat AI-generated code as a black box. Understanding the implementation helps with:

• Debugging issues
• Optimizing performance
• Adding custom features
• Maintaining the code

4. Leverage AI for documentation

The AI generated excellent documentation, but I enhanced it with:

• Real-world usage examples
• Troubleshooting guides
• Performance tuning tips
• Deployment considerations

Production deployment considerations

The Cursor assistant also provided the following code snippets as recommendations for a production deployment. It even included the necessary deploy command: 

Configuration management

 #Environment-specific configurations
if os.getenv('ENVIRONMENT') == 'production':
   rate_limit_pause = 1.0   More conservative in production
   max_batches = None   Process full dataset
else:
   rate_limit_pause = 0.5   Faster for development
   max_batches = 10   Limited for testing

Monitoring and alerting

# Enhanced logging for production
log.info(f"Processing batch {batch_count}: {len(results)} records")
log.info(f"Total records processed: {skip}")
log.info(f"API response time: {response_time:.2f}s")

Error handling in production

 #Graceful degradation
try:
   response_data = fetch_data(base_url, params, api_key)
except RuntimeError as e:
   log.severe(f"Critical API failure: {e}")
    Send alert to monitoring system
   send_alert(f"FDA API connector failed: {e}")
   raise

Deployment command

fivetran deploy --api-key $FIVETRAN_API_KEY --destination "csg_auto_test" --connection "sdk_video" --configuration "configuration.json"

All told, we achieved a good balance of time saved as well as quality.

• Time Investment: <30 minutes for incremental data load, successful local debug, DuckDB data review, and deployment to Fivetran. Consider + 1 hour for review, testing, and refinement.
• Traditional development: Estimated 2-3 days for equivalent functionality
• Quality: Production-ready code with enterprise-grade features
• Maintainability: Well-documented, modular, and extensible

Conclusion: The future of data engineering is now

This experience demonstrated that AI-assisted development isn't just about speed. It's about quality and consistency. The AI-generated code incorporated industry best practices and referenced system instructions with template code to produce a new solution. The key takeaways of the experience were:

1. AI is a force multiplier: It doesn't replace developers but amplifies their capabilities
2. Prompt engineering is critical: The quality of input directly affects output quality
3. Review is essential: Always understand and validate AI-generated code
4. Documentation matters: AI can generate excellent documentation, saving significant time
5. Production readiness: AI can incorporate production patterns from the start
   

For the future, I'm exploring how to:

• Generate comprehensive test suites with AI
• Create monitoring and alerting configurations
• Build deployment pipelines
• Develop custom transformations for specific business needs
• Dynamically create dashboards and data insights
  

The FDA Food Enforcement connector is now running in production, processing records daily with 99.9% uptime. The AI-assisted development process not only accelerated delivery but also resulted in a more robust and maintainable solution.

The question isn't whether AI will change how we build data infrastructure—it's how quickly we will adapt to leverage its full potential.

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Ready to build your own AI-assisted connector? Interested in vibe-coding? Start with the Fivetran Connector SDK documentation and experiment with Cursor AI to accelerate your development process today!

 Resources

- Fivetran Connector SDK guide

- Fivetran Connector SDK AI system instructions

- Connector SDK Cursor tutorial

- Fivetran YouTube

- Complete FDA connector code

- Cursor AI documentation 

- FDA food enforcement API documentation