We are excited to announce the winners of the inaugural Databricks Free Edition Hackathon. This hackathon attracted data and AI practitioners from over 16 countries, showcasing innovation in AI, data engineering, data analytics and more.
During the hackathon, participants used the free edition to create a five-minute demo that demonstrated a range of use cases, including an end-to-end car sales analytics platform, a recovery-augmented (RAG) workflow for product documentation, and a data engineering assistant that allows users to automate and simplify engineering workflows. The submitted demos were evaluated based on the following evaluation criteria:
- Technical Complexity and Performance
- creativity and innovation
- Presentation and Communication
- impact and value of learning
There were many impressive project presentations; However, three projects emerged.
Congratulations to the winners of the inaugural Free Edition Hackathon for building data and AI applications that demonstrate technical excellence, creativity, and more!
Winner:
Narendra Kumar
🏆 First place
Narendra made widmindAn automated workflow for technical demo videos on YouTube. The project ingests raw unstructured video, extracts content, organizes it into a structured knowledge base, and returns insights to a fictional company called DataTuber. This shows how the free version helps creators and teams transform large amounts of media into searchable and actionable data.
zoe booth
🏆 second place
zoe built a space weather analysis system Which supports power grid operators. Its solution predicts grid failures caused by solar flare events and provides a seven-day forecast, risk thresholds and recommended actions. Workflow uses data engineering and ML features in the free version to help improve the resiliency of critical infrastructure.
Hasnat Abdul
🏆 3rd place
beauty Created a recipe recommendation engine Using NLP. They took raw recipe data, prepared and structured it, and trained a model to group recipes based on shared themes and flavor profiles. Users query the system in natural language to receive personalized suggestions. This shows how the free version supports text-based workflows and behavioral recommendation systems.
honorable mentions:
Lucas Frolio and Travis Weissman: AI-powered biomedical research assistant agent is an agent that helps researchers ingest, search, and analyze biomedical literature at scale – turning reams of academic data into actionable insights in seconds.
Angie Shin and Hyeju Jung , End-to-End Wildfire Analysis System Focused on integrating fragmented environmental datasets across Canada to support more accurate wildfire monitoring and analysis
Brahma Reddy Katam , The future of movie discovery is an AI-powered movie recommendation app that uses the Netflix Movies dataset, PySpark, and embedding models to suggest movies based on the user’s mood and natural-language input.
Dinesh S , AI-powered data engineering assistant Which allows business users to update configuration tables, trigger ETL pipelines, and run data validation using natural language.
These projects demonstrate how students and developers use the free version to accelerate their learning and develop practical data and AI applications. The series of submissions highlights what’s possible when ideas are easy to try and workflows are simple to set up.
Interested in exploring the free version? Sign up today.
