Deepnote

What is Deepnote?

Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore and analyze it with real-time collaboration and versioning, and easily share and present the polished assets to end users.

Deepnote is a tool in the Development & Training category of a tech stack.

Key Features

Real-time collaborationSharingCommentsCode reviewVariable explorer, AutocompletionCommand paletteKeyboard shortcuts

Deepnote Pros & Cons

Pros of Deepnote

✓Fully managed notebook
✓GitHub / Gitlab integration
✓Real-time collaboration
✓Browser based
✓Cloud-based

Cons of Deepnote

No cons listed yet.

Deepnote Integrations

MongoDB, GitHub, Amazon Redshift, Google BigQuery, Snowflake and 3 more are some of the popular tools that integrate with Deepnote. Here's a list of all 8 tools that integrate with Deepnote.

Deepnote Discussions

Discover why developers choose Deepnote. Read real-world technical decisions and stack choices from the StackShare community.

Jan Vlnas

Senior Software Engineer

Oct 6, 2022

Needs adviceon

OpenRefine

Jupyter

Databricks

From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.

I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.

Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.

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Deepnote Alternatives & Comparisons

What are some alternatives to Deepnote?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

NumPy

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Jupyter

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.

Deepnote

What is Deepnote?

Key Features

Deepnote Pros & Cons

Pros of Deepnote

Cons of Deepnote

Deepnote Integrations

Deepnote Discussions

Deepnote Alternatives & Comparisons

TensorFlow

NumPy

Jupyter

Try It

Adoption

On StackShare

Pandas

PyTorch

scikit-learn

Deepnote Integrations

Deepnote Discussions

Try It

Adoption

On StackShare