This blog post is Human-Centered Content: Written by humans for humans.

It was almost inevitable that I’d become a ski addict, born in a small Northern California town just two hours from Tahoe with a dad who worked at a ski resort. Over the years, I have lived up and down the West Coast, exploring an incredible variety of ski resorts. I have come to appreciate places like Mt. Bachelor and Whistler Blackcomb for their vast terrain and breathtaking size, and smaller gems like Willamette Pass for their low crowds and untouched snow in the right years.

Planning a ski trip involves countless factors. What will the weather be like? Do they have enough runs that match my skill level? How long is the drive? How much will it cost? The list never seems to end.

To help answer those questions and to finally prove to my friends which mountain truly deserves the title of the best, I created the Ski Buddy Dashboard. It is designed to give skiers and snowboarders a deeper understanding of the top resorts across the Western United States and Canada.

Sourcing the Data

I was stoked to use this project as an opportunity to explore n8n, a workflow automation tool, to orchestrate data collection from multiple APIs and load it into Snowflake, all while keeping the data current.

The APIs I worked with, and would like to acknowledge, include:

1. OpenStreetMap (OSM) — For geocoding and location data
2. Visual Crossing Weather API — For historical weather data (snow depth, snowfall, temperature, weather conditions)
3. Open-Meteo API — For weather forecast data (16-day forecasts)
4. Nager.Date API — For US and Canada holiday data
5. OpenRouteService API — For calculating drive distances and times between cities and resorts

I created four workflows in n8n to gather all the data needed for my dashboard and to load it into Snowflake.

Workflow 1: Holiday Data Loader

Runs annually

Populates a 12-year rolling window of U.S. and Canadian holidays, ensuring the dashboard recognizes holiday periods, which typically correspond with higher resort crowds.

Workflow 2: Historical Snow Data Backfill

Runs one time

Pulls more than two years of historical weather data from both Visual Crossing and Open-Meteo, including snowfall, snow depth, temperature and general weather conditions. Since Tableau Public does not support live data connections, I used this historical backfill approach to allow users to explore realistic past conditions and still experience how the live dashboard behaves.

Workflow 3: Monthly Resort Data Loader

Runs monthly

Populates resort characteristics that rarely change, including:

• Lift counts by type
• Run counts by difficulty level
• Drive distances from major cities
• Population density within proximity radiuses

Note: This workflow initially required nested looping to iterate through every resort and each destination city for drive-time calculations. However, n8n does not support nested loops directly. The workaround is to design your workflow as if you were using nested loops, then highlight the nodes in the inner loop, right-click and select Convert nodes to sub-workflow. This creates a sub-workflow that functions correctly for every combination of the two variables. In my case, the ResortDriveSummary sub-workflow performs this nested loop behavior.

Workflow 4: Daily Weather Update

Runs daily

Keeps the dataset current by fetching the previous day’s actual weather conditions from Visual Crossing and the next 16 days of forecast data from Open-Meteo. Both APIs provide snowfall, temperature and weather conditions, while Visual Crossing also includes snow depth. This ensures the dashboard always reflects both current and forward-looking snow and weather data.

The Dashboard

Unfortunately, Tableau Public doesn’t support live data connections at the time of writing, so the interactive version below is a static snapshot covering November 2024 through April 2025. However, it functions exactly the same as my live version.

Here’s how you use it:

1. Answer a few questions: Pass type, home city, skill level and date.
2. Get context: The right side shows aggregate snowfall, crowd estimates and a map of resort locations to help inform your choices.
3. Hit submit: You get a ranked bar chart of resorts sorted by overall score.
4. Click a resort: Dive into a detailed view showing how that mountain scores on five key factors — customized to your inputs.

How Scoring Works

Each resort gets an overall score (0-100) based on five factors:

1. Drive Time (35%)
   The most influential. Mountains within two hours score 100. Resorts over eight hours away score lower. But even distant mountains get a minimum ten points if they crush it elsewhere.
2. Snow Quality (20%)
   Combines recent snowfall (last three days, 60%) and current base depth (40%). Fresh powder matters most, but a deep base keeps conditions consistent.
3. Terrain Match (20%)
   How well does the mountain fit your skill level?
   +Lift vertical (25%): More vertical = more variety
   +Total run length (35%): More terrain = more exploration
   +Difficulty match (40%): Personalized to you. Experts get full credit for expert/advanced runs, partial for intermediate, zero for beginner.
4. Crowd Factor (15%)
   Estimates how busy it’ll be based on holidays, day of the week and resort capacity vs. nearby population. Lower crowds = higher scores.
5. Price Value (10%)
   If you have an Epic or Ikon pass, those resorts score 100 as you no longer need the day pass! Otherwise, cheaper tickets score higher and vice versa.

The radar chart breaks everything down visually, allowing you to see exactly why each resort scored the way it did and to make trade-offs based on what matters most to you. I created this with the help of a blog by The Flerlage Twin which you can find here.

Ultimately, this dashboard turns publicly available data into actionable insights. Whether you’re chasing powder, maximizing a weekend or proving to your friends which mountain is actually the best, it helps you make smarter decisions. See you on the slopes!