I was chatting with a client the other day who wanted a weather dashboard for his high-traffic travel site. He didn’t just want the numbers; he wanted to tell his users what to wear before they headed out. “Just write a bunch of if-statements for the temperature,” he told me. Yeah, right. Good luck maintaining that when you have 50 different weather codes, humidity factors, and wind chills to consider. It’s a nightmare. Total mess. Period.
Instead of writing brittle, hardcoded logic that breaks the second a new weather condition pops up, I decided to build a proper AI-powered weather pipeline. By leveraging Databricks for the heavy lifting and GPT-4o-mini for the “common sense” layer, we can turn raw JSON into actual advice. This is similar to the fast AI prototyping techniques I’ve used for other enterprise projects.
Building the AI-Powered Weather Pipeline Architecture
My first thought was to just fetch the data and run it through a local script. But that’s amateur hour. If you want this to be production-ready, you need orchestration. We used the OpenWeatherMap API for the raw data and stored it in the Databricks Unity Catalog. Trust me on this: having a structured Silver Layer for your data is the only way to keep your sanity as the project scales. If you don’t, your AI features might break user trust due to stale or inconsistent data.
Here is how we modularized the extraction phase using a clean Python class. I always prefix my helper functions to avoid collisions in shared notebooks.
class bbioon_Weather_Service:
def __init__(self, api_key):
self.api_key = api_key
def bbioon_fetch_current_weather(self, city, country):
url = f"https://api.openweathermap.org/data/2.5/weather?q={city},{country}&APPID={self.api_key}&units=imperial"
response = requests.get(url)
if response.status_code != 200:
raise Exception(f"API Error: {response.status_code}")
return response.json()The Transformation Layer: GPT-4o-mini Integration
The “why” behind this AI-powered weather pipeline is the transformation step. Instead of parsing the “Clouds” or “Rain” string ourselves, we pass the temperature and conditions to GPT-4o-mini. It’s cheap, it’s fast, and it handles the nuance of “dressing for the weather” better than any regex I’ve ever written. Here’s the kicker: we save the raw JSON as our “Source of Truth” in the Unity Catalog before we even touch it with AI. Safety first.
# Calling the LLM for dressing suggestions
def bbioon_get_ai_suggestion(weather_desc, temp):
client = OpenAI(api_key=dbutils.widgets.get('OPENAI_API_KEY'))
prompt = f"The weather is {weather_desc} at {temp}F. Suggest what to wear in one short sentence."
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.contentOrchestration and the “Lakehouse” Way
We don’t just run this once. We schedule it. Using Databricks Jobs, we set the notebook to trigger every hour. This ensures the dashboard stays fresh. We load the final, cleaned data into a Delta Table using an “append” mode. This creates an audit trail. If the LLM ever hallucination and tells someone to wear a parka in 90-degree heat, we can look back and see exactly what the API sent us at that timestamp.
Look, this stuff gets complicated fast. Managing Databricks Unity Catalog and LLM tokens while keeping costs down takes a bit of experience. If you’re tired of debugging someone else’s mess and just want your data pipeline to work without constant babysitting, drop me a line. I’ve probably seen it before.
So, What’s the Point?
- Raw data is cold: Users want actionable insights, not just numbers.
- LLMs beat If-Statements: Use GPT-4o-mini for complex conditional logic that would be a nightmare to hardcode.
- Persistence matters: Always save your raw API responses to a Silver layer like Delta Tables for auditing.
- Automation is king: If it’s not scheduled, it’s not a pipeline. It’s just a script.
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