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Databricks Notebooks vs Deepnote:
a side-by-side comparison for 2024

Comparing two data science notebooks.

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Databricks Notebooks

Collaborate across engineering, data science, and machine learning teams with support for multiple languages, built-in data visualizations, automatic versioning, and operationalization with jobs.

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Deepnote

Deepnote is a new kind of data notebook that’s built for collaboration — Jupyter compatible, works magically in the cloud, and sharing is as easy as sending a link.
Get started – it’s free
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Databricks Notebooks vs Deepnote

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.

AI Capabilities:
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.

Jupyter Compatibility:
Both platforms are fully compatible with Jupyter, allowing seamless notebook uploading and usage.

Exploratory Coding:
Both platforms support Python and R. Databricks utilizes Jedi for coding assistance, while Deepnote relies on Codeium's AI code completion. Databricks additionally supports Scala.

Data Connection:
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.

Data Publishing:
Both platforms offer reporting and app tools, allowing scheduling, dashboard creation, and interactive dashboarding with fine-grained permission settings.

Collaboration:
Both platforms prioritize collaboration, featuring fine-tuned permissions, collaborative notebooks, multi-user editing, and commenting.

Pricing:
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.

Databricks provides a 14-day free trial and follows a pay-as-you-go model for pricing.

Read more about Deepnote, and Databrick's pricing.

In Conclusion:
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.



Databricks Notebooks

Deepnote

Setup

Is it managed?

Is it managed?

Fully managed (setup in minutes)
Fully managed (setup in minutes)

Can you self-host?

Can you self-host?

You can self-host (setup in hours)
No, you must use a managed offering

Features

Is it Jupyter compatible?

Is it Jupyter compatible?

Jupyter-compatible
Jupyter-compatible

Programming languages

Programming languages

Jupyter languages (e.g. Python, R)
Jupyter languages (e.g. Python, R)
SQL

What kind of data sources can you connect to?

What kind of data sources can you connect to?

Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
Connect to data warehouses (Databricks)
Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
Connect to data warehouses (AWS, GCP, etc.)
Connect to databases (Postgres, MongoDB, etc.)
Provided file storage

What kind of data visualization can you do?

What kind of data visualization can you do?

Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)
UI for building charts
Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)
UI for building charts

Reactivity

Reactivity

No reactivity, you decide the execution order
Full, realtime reactivity

Notebook scheduling

Notebook scheduling

Notebook scheduling is built in
Notebook scheduling is built in

Management

Reproducibility

Reproducibility

Environments are reproducible by default
Environments are reproducible by default
Run notebooks in containers

Version history

Version history

Version history is built in
Version history is built in

Collaborative editing

Collaborative editing

Multiple editors at the same time
Multiple editors at the same time

Comments

Comments

Comment on items within a notebook
Comment on items within a notebook

Notebook organization

Notebook organization

View notebooks in a list
View notebooks in a tree, like a wiki

Licensing

License

License

Proprietary
Proprietary

Price

Price

Free tier
Pay for compute
Free tier
Pay for compute
Pay-per-user

That’s it, time to try Deepnote

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