Deepnote AI vs. Jupyter AI: Which fits your data workflow better?

Comparing Deepnote AI and Jupyter AI, we dive deep into how each platform integrates into your data workflow, evaluating features, user experience, and contextual intelligence.

Move fast and operationalize things with Deepnote & Materialize

We're excited to announce our integration with Materialize. Learn about the opportunities this collaboration opens for your development workflow.

Deepnote Achieves Google Cloud Ready - AlloyDB

Deepnote is proud to announce AlloyDB Ready designation.

Deepnote Achieves Google Cloud Ready - Cloud SQL Designation

Deepnote is proud to announce Cloud SQL Ready designation.

Deepnote Achieves Google Cloud Ready - BigQuery Designation

Deepnote is proud to announce it's designation as Google Cloud Ready - BigQuery

Using Deepnote AI & Modelbit to build an ML model and deploy it to production

A new era for data work: autonomous Deepnote AI

Deepnote's autonomous AI assistant is reshaping data workflows by independently handling complex tasks, from start to finish.

Introducing Deepnote AI

Deepnote's AI Copilot, with its efficient and contextual code suggestions, is paving the way for a future of AI-powered data exploration in notebooks.

7 best Colab alternatives in 2023

Google Colab is a widely popular tool for machine learning and data science, especially among beginners and educators. However, it does come with its strengths and weaknesses.

How teams use notebooks to streamline data sharing

Modern notebooks make data sharing a breeze. Here’s how leading data teams use them to dish out actionable insights.

Collaborative Jupyter notebook: a checklist for data teams

Searching for a collaborative Jupyter notebook? Start by understanding which boxes need to be checked for your data team.

Why it’s time to take your Jupyter notebook online

Locally hosted notebooks aren’t built for the way modern data teams work. Learn why you should bring your Jupyter notebook online.

Unlock your SQL superpowers with Deepnote & Dremio

We're excited to announce our integration with Dremio. Learn more about what this means for your SQL analytics workflows.

From spreadsheets to notebooks: the BizOps playbook

BizOps is all about optimization. Learn how to improve your analytical workflows by replacing spreadsheets with notebooks.

Why the best SQL notebook is also a Python notebook

Don’t sell yourself short with your SQL notebook. Learn why it pays to choose a notebook that unlocks the power of SQL and Python together.

How teams use notebooks to boost data collaboration

Modern notebooks are collaborative by design. Here’s how leading data teams use them to improve data collaboration.

One team, infinite outputs: how Ramp achieves data platform optimization

Transforming raw data into business value is a tall order. Find out how Ramp helps its data platform team deliver with Deepnote.

How data teams collaborate (& what's standing in the way)

Data collaboration comes in many forms — and with many challenges. Learn how data teams can break down collaboration barriers with the right tools and tactics.

Tutorial: how to speed up pandas with NumPy methods

Sometimes pandas needs a performance boost. Learn how to go from pandas to NumPy methods to increase speed.

The good, the bad & the ugly: how to share Jupyter notebooks

Sharing is caring when it comes to data analysis. Check out your options for sharing Jupyter notebooks — and the pros and cons of each.

From insights to outcomes: how Gusto democratizes data analytics

Visibility and accessibility are key to driving results. Find out how Gusto centralized its analytics workflows and empowered all team members with Deepnote.

3 data smells that mean you need a Jupyter notebook alternative

Well-known isn’t the same as best-in-breed. Find out which signals suggest it’s time to explore a Jupyter notebook alternative.

Smarter, faster, better: introducing interactive exploration & other power-ups to Deepnote charts

Data visualization is about so much more than painting static pictures. See how Deepnote’s no-code charting experience brings you closer to your data.

Tutorial: how to query pandas DataFrames with SQL

Enjoy the best of both worlds. Learn how to work with Python and SQL in pandas Dataframes.

How teams use notebooks to power data exploration

Modern notebooks are built for data exploration. Here’s how leading data teams use them to supercharge exploratory analysis.

Exploratory programming: what it is, why it matters & what it requires

Exploring data isn’t the same as developing software. Learn why data teams should embrace tools built for exploratory programming.

Minimum viable (data) product: how Slido brings a product mindset to analytics engineering

Delivering business-friendly metrics isn’t always easy. Find out how Slido conquered the challenges with Deepnote and dbt.

Explore, collaborate, share: how Webflow optimizes data workflows

Teamwork makes the dream work. Find out how Webflow overcame collaboration challenges inside and outside its data team with Deepnote.

Tutorial: semantic search using Faiss & MPNet

Semantic search is changing how we find information. Learn how to get started using a data notebook.

Tutorial: cleaning & tidying data in pandas

Don't let sloppy data get you down. Learn how to clean and tidy up messy data with pandas DataFrames.

To all those forgotten data transformations, we salute you

Not all organizational knowledge makes its way into dbt. Let's explore some of these sources of knowledge, why they exist, and their role in the data stack.

Data visualization for everyone: Meet the new chart block

See how we redesigned our no-code charting experience to help streamline data visualization.

Announcing Deepnote's layoff relief program

We’re making Deepnote free for anyone impacted by recent layoffs in an effort to help with portfolio development and showcasing work to employers.

Tutorial: filtering with pandas

Learn how to leverage the Python pandas framework to filter data for a wide range of use cases.

The past, present & future of notebooks

Data notebooks have come a long way since their introduction. Here's how we got here, where the market is at, and predictions for the future.

Announcing the dbt Semantic Layer for data notebooks

We're excited to announce our native integration with the dbt Semantic Layer. See what it means and hear the stories of the data teams using it.

Bringing a richer text experience to data notebooks

Learn why we elevated text editing from an afterthought to a first-class citizen in Deepnote notebooks.

Supercharge your Microsoft SQL Server workflows with Deepnote

Deepnote’s Microsoft SQL Server integration helps data teams query, extract, analyze, and model data from the comfort of a notebook environment.

Bridging the exploratory analytics gap with Deepnote for Snowflake

See how Deepnote — now a Snowflake Select Technology Partner — and Snowflake work together to power your SQL and Python workflows.

Deploy ML models to Snowflake with Deepnote & Modelbit

Learn how to build and train a machine learning model in Deepnote and deploy it to Snowflake using Modelbit.

Bringing ClickHouse performance to the comfort of data notebooks

With Deepnote and ClickHouse, data teams can query large data sets, extract relevant data, and analyze and model data — all within the comfort of a notebook.

Snowpark for Python: Bring Python to your data warehouse with Deepnote

See how you can use Snowpark's superpowers in Deepnote — its Python-ready partner.

Deepnote is out of beta!

We’re removing the “beta” label and making Deepnote generally available for the world’s best data teams.

Welcome to Deepnote workspaces

We're excited to present Deepnote workspaces — a collaborative space for data teams of all sizes to organize, share, and scale knowledge.

Profiling a Node.js blocked event loop in production

See how we set up just-in-time profiling of our Node.js app to tackle a stuck event loop.

SQL just got machine learning

The people with the deepest understanding of company data typically speak SQL. See how MindsDB puts machine learning at their fingertips.

Deepnote raises a $20M series A led by Accel & Index

We're excited to announce that we raised a $20M Series A led by Accel and Index. Find out why the world’s best VCs are doubling down on our vision.

How not to draw an owl

Ever been excited to learn something before realizing you’re not familiar with the prerequisites? See how Deepnote supports effective, context-based learning.

Deepnote's year in review: 2021

Here’s a recap of how the Deepnote team, community, and product evolved in 2021. We wish you a great start to 2022!

Bringing analytics to Notion with Deepnote

Learn how to build charts over Notion databases and keep your analytics & storytelling all in one place.

Deepnote & Webex light up Times Square

See Deepnote on one of New York's biggest screens — the NASDAQ Tower in Times Square — as part of the Webex App Hub campaign.

Data science beyond data science teams

We’re excited to launch Deepnote for Teams to help give anyone in any organization a seat at the data table.

Survey of storage in data science notebook platforms in 2021

We looked at popular notebook platforms and compared their storage solutions, both in managed and on-premise versions. Here are the results.

Building a design system at a startup

Learn how to build a design system at a fast-moving startup and leverage all the benefits it offers.

Deepnote emerges from stealth with YC, Index & Accel leading our seed round

We’re excited to announce that we raised $3.8M from YC, Index, and Accel to build a new kind of data notebook. Here’s our story.