Databricks vs. Deepnote: A 2023 Comparison
Databricks primarily utilizes notebooks for data science and machine learning workflows, offering real-time coauthoring, automatic versioning, and built-in data visualizations.
Deepnote is a collaboration-oriented data platform, providing a cloud-based workspace for data exploration, collaboration, interactive chart and dashboard creation, and machine learning model development and deployment.
Both platforms offer AI assistance for coding. Both Databricks and Deepnote boast an advanced AI agent capable of creating SQL, Python, and text blocks, ensuring expected results and self-correction.
Both platforms are fully compatible with Jupyter, allowing seamless notebook uploading and usage.
Both platforms provide a wide array of built-in data connectors for major cloud platforms, simplifying data source access.
Data Interaction and Visualization:
Both platforms include Notebook SQL blocks with code completion and built-in data frame outputs. They also offer built-in charting solutions and support various Python charting libraries.
Both platforms offer reporting and app tools, allowing scheduling, dashboard creation, and interactive dashboarding with fine-grained permission settings.
Both platforms prioritize collaboration, featuring fine-tuned permissions, collaborative notebooks, multi-user editing, and commenting.
Deepnote offers a free tier, a two-week trial with no credit card requirement, and permanent freemium options. Deepnote's team and enterprise plans combine seat and usage-based pricing.
Both Databricks and Deepnote are collaboration-focused data platforms with similar feature sets. The choice depends on specific requirements. Databricks excels with Scala support, while Deepnote offers a more advanced AI agent, and extensive integrations.