Project Competition
Build a data engineering project with Bruin and compete for prizes. Vote for your favorites and share the best projects with the community.
Participation
1 month Claude Pro subscription
Limited to the first 100 submissions
How to qualify
- Use Bruin for ingestion, transformation, orchestration, and analysis (using the AI data analyst)
- Post your project in the #projects channel on Bruin Data Community Slack
- Include a GitHub repo with a README
- Submit your project via our website
Top 3 Projects
1 year Claude Pro subscription each
Outstanding Project
Mac Mini
How to qualify
- Use Bruin for ingestion, transformation, orchestration, and analysis
- Post in #projects on Bruin Data Community Slack
- GitHub repo with README
- Submit via our website
- Create a LinkedIn post explaining which Bruin features you used, your design choices, and how Bruin compares to other tools
- Include screenshots of analysis done with the Bruin AI data analyst
- Top 10 posts by likes enter a random draw
Important: The identity of all participants is subject to verification to ensure fair competition and prevent cheating, plagiarism, and spam.
Note: The free Claude Pro subscription for participation is limited to the first 100 project submissions. After that, you can still compete for the Top 3 and Outstanding Project prizes.
How to Build Your Project
From zero to a complete data project in four steps — plus an optional cloud deployment.
Set Up Your Project
- -Install Bruin:
curl -LsSf https://getbruin.com/install/cli | sh - -Initialize a project:
bruin init empty my-project - -Choose your database: DuckDB (local, zero setup) or BigQuery (cloud)
- -Configure your connection in
.bruin.yml
Ingest Your Data
Three ways to get data into your project:
Ingestr YAML Assets
Built-in connectors for 100+ sources. Just define a YAML file.
name: chess.profiles type: ingestr parameters: source_connection: chess source_table: profiles destination: duckdb
DuckDB Read from URL
Read CSV or Parquet files directly from public URLs.
SELECT * FROM read_parquet( 'https://...data.parquet' );
Python Extract
Write a Python script that returns a DataFrame.
def materialize(): df = pd.read_csv(url) return df
Free dataset ideas
- -Chess.com — built-in ingestr source
- -BigQuery public datasets — Wikipedia, GitHub, Stack Overflow
- -NYC Taxi — Parquet files via URL or Python
- -Frankfurter API — exchange rates via Python
- -GitHub Archive — public event data via BigQuery or Parquet
- -Google Sheets — any spreadsheet via ingestr
Transform with SQL
- -Write SQL assets to clean, join, and aggregate your raw data
- -Materialize results as tables or views for downstream use
- -Add quality checks (not_null, unique, accepted_values) to validate your data
- -Run the pipeline:
bruin run .
/* @bruin
name: analytics.monthly_summary
type: duckdb.sql
materialization:
type: table
@bruin */
SELECT date_trunc('month', created_at) AS month,
count(*) AS total_records
FROM raw.my_data
GROUP BY 1;SQL assets guide → · Quality checks → · NYC Taxi Tutorial: Build the Pipeline →
Analyze with AI
Build a context layer and let AI understand your data:
- -Run
bruin ai enhance assets/to auto-generate descriptions, quality checks, and tags for all your assets - -Set up Bruin MCP in your IDE so AI agents can query and understand your data:
Cursor / Claude Code
claude mcp add bruin \ -- bruin mcp
VS Code
"bruin": {
"command": "bruin",
"args": ["mcp"]
}Codex CLI
[mcp_servers.bruin] command = "bruin" args = ["mcp"]
- -Ask Cursor, Claude Code, or Codex to analyze your data, find patterns, and generate insights
- -Alternatively: deploy to Bruin Cloud and use the AI Chat or AI Dashboard features for instant analysis
Bruin MCP setup → · AI enhance docs → · NYC Taxi Tutorial: Build with MCP →
Deploy to Bruin Cloud Optional
Take your pipeline to production with scheduling, monitoring, and AI-powered analysis.
- -Sign up for free at getbruin.com — no credit card required
- -Free tier includes credits to schedule and run your pipelines in the cloud
- -Access the AI Data Analyst — ask questions about your data in natural language from Slack, Teams, or the browser
- -Use the AI Dashboard Builder — generate dashboards with KPIs and charts from a single prompt
Cloud onboarding video → · AI Data Analyst tutorial → · AI Dashboard Builder tutorial →
Learning Resources
Everything you need to get started with Bruin.