Announcing the winners of the inaugural Databricks Free Edition Hackathon

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 across AI, data engineering, data analysis, and more.

During the hackathon, participants utilized the Free Edition to build a five-minute demo that showcased a range of use cases, including an end-to-end car sales analytics platform, a retrieval-augmented (RAG) workflow for product documentation, and a data engineering assistant that allows users to automate and simplify engineering workflows. The submitted demos were judged based on the following evaluation criteria:

• Technical Complexity & Execution
• Creativity & Innovation
• Presentation & Communication
• Impact & Learning Value

There were many impactful project submissions; however, three projects stood out.

Congratulations to the winners of the inaugural Free Edition Hackathon for building data and AI applications that demonstrated technical excellence, creativity, and much more!

Winners:

Narender Kumar

🏆 ​​First place

Narender built VidMind, an automated workflow for technical demo videos on YouTube. The project ingests raw unstructured video, extracts the content, organizes it into a structured knowledge base, and returns insights for a fictional company named DataTuber. This shows how Free Edition helps creators and teams turn large volumes of media into searchable and actionable data.

Zoe Booth

🏆 ​​Second place

Zoe built a space weather analysis system that supports power grid operators. Her solution predicts grid failures caused by solar flare events and provides seven day forecasts, risk thresholds, and recommended actions. The workflow uses data engineering and ML features in Free Edition to help improve resilience for critical infrastructure.

Hasnat Abdul

🏆 ​​Third place

Hasnat built a recipe recommendation engine using NLP. He ingested raw recipe data, prepared and structured it, and trained a model to group recipes by shared themes and flavor profiles. Users then query the system in natural language to get personalized suggestions. This shows how Free Edition supports text based workflows and practical recommendation systems.

Honorable mentions:

Lucas Frolio & Travis Wessman: AI-powered Biomedical Research Assistant Agent is an agent that helps researchers ingest, search, and analyze biomedical literature at scale—turning mountains of academic data into actionable insights in seconds.

Angie Shin & HyeJu Jung - End-to-end wildfire analytics system focused on unifying fragmented environmental datasets across Canada to support more accurate wildfire monitoring and analysis

Brahma Reddy Katam - Future of Movie Discovery is an AI-powered movie recommendation app that utilizes the Netflix Movies dataset, PySpark, and embedding models to suggest movies based on a user’s mood and natural-language input.

Dinesh S - AI-driven data engineering assistant that allows business users to update configuration tables, trigger ETL pipelines, and run data validations using natural language.

These projects demonstrate how students and developers utilize the Free Edition to accelerate their learning and develop practical data and AI applications. The range of submissions highlights what is possible when ideas are easy to try and workflows are simple to set up.

Interested in exploring Free Edition? Sign up today.