Posting in the Big Data Processing and Distribution category on G2 for data engineering and analytics teams at the decision point between these two platforms specifically on the collaborative notebook experience. Both support SQL and Python workflows, but they have meaningfully different notebook architectures, collaboration models, and language breadth.
Feature: Databricks provides native multi-language notebooks where Python, SQL, and Scala cells coexist in the same notebook. Collaborative real-time notebook editing allows multiple team members to work simultaneously. Snowflake Notebooks support Python and SQL, and the Snowpark Python API allows Python-based transformation logic that runs in the Snowflake engine. Having SQL and Python in the same environment is a genuine convenience rather than needing to export data to a separate tool.
Integration: Databricks notebooks integrate natively with MLflow for experiment tracking and Delta Lake for table management, the same notebook used for ETL development becomes the ML training environment without switching tools. Snowflake notebooks integrate with Streamlit for application development and the Snowflake ecosystem for data sharing and governance. Git integration is available in both, but Databricks Repos provides tighter Git workflow management for engineering teams with CI/CD requirements.
The honest comparison on collaborative notebooks: Databricks is the stronger choice for teams that need genuine multi-language notebook environments with Scala, Python, and SQL in the same workspace, tight ML integration, and real-time collaborative editing. Snowflake is the stronger choice for SQL-primary teams that want Python as an extension language without adopting Spark's operational complexity, the notebook experience is simpler but the compute model is more predictable.
What was the workflow that made one platform's notebook environment clearly better for your team's daily development pattern — and was it the language support, the real-time collaboration, the compute behaviour, or the integration with downstream tools that drove the decision?
##### Post Metadata - Posted at: about 1 month ago - Author title: Marketing Executive - Net upvotes: 1 ## Comments ### Comment 1 <p>For a mixed data engineering and ML team, language support would probably be the deciding factor. Being able to keep SQL, Python, and Scala in one notebook cuts a lot of context switching, and Databricks feels better suited to that daily workflow.</p> ##### Comment Metadata - Posted at: about 1 month ago - Author title: Marketer ## Related discussions - [How well does Trello scale into a larger team?](https://www.g2.com/discussions/1-how-well-does-trello-scale-into-a-larger-team) - Posted at: over 13 years ago - Comments: 6 - [Can we please add a new section](https://www.g2.com/discussions/2-can-we-please-add-a-new-section) - Posted at: over 13 years ago - Comments: 0 - [Quantifiable benefits from implementing your CRM](https://www.g2.com/discussions/quantifiable-benefits-from-implementing-your-crm) - Posted at: over 13 years ago - Comments: 4