Updates and thoughts about Deepnote and data science notebooks from the team building the future of IDEs for data scientists.
– by Eric on January 24, 2023
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.
– by Jakub on January 23, 2023
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.
– by Eric on January 10, 2023
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.
– by Eric on January 3, 2023
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.
– by Mark on December 21, 2022
Tutorial: semantic search using Faiss & MPNet
Semantic search is changing how we find information. Learn how to get started using a data notebook.
– by Mark on December 16, 2022
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.
– by Allan and Katie on November 18, 2022
To all those forgotten data transformations, we salute you
The goal of any data practitioner is ultimately to create and disseminate organizational knowledge. But organizational knowledge exists everywhere—and not all of it makes its way into a standardized system (like dbt). Let's explore some of these sources of knowledge, appreciate why they exist, and imagine their future role in the data stack.
– by Gabor on November 16, 2022
Data visualization for everyone: Meet the new chart block
How we redesigned our no-code charting experience.
– by Jakub on November 15, 2022
A little relief
We’re making Deepnote free for anyone impacted by recent layoffs in an effort to help with data science portfolio development and showcasing work to employers.
– by Mark on November 11, 2022
Tutorial: filtering with pandas
In this post we leverage the Python Pandas framework to filter data for a very wide range of use cases. We will walk through a number of examples that use Pandas to filter data.
– by Jakub on November 2, 2022
The past, present, & future of notebooks
Data science notebooks have come a long way since first introduced back in 1988. Here's the 101 on how we got here, where the market is at, and predictions for the future.
– by Elizabeth on October 18, 2022
dbt Semantic Layer for data notebooks
Announcing our native integration of the dbt Semantic Layer, what it means, and the stories of data teams using it.
– by Gabor on September 15, 2022
Bringing a richer text experience to data notebooks
Why we elevated text editing from an afterthought to a first class citizen in Deepnote notebooks.
– by Lukas on August 11, 2022
Supercharge your Microsoft SQL Server workflows with Deepnote & Python
With Deepnote’s Microsoft SQL Server integration, data teams can efficiently query, extract, analyze, and model data stored in Microsoft SQL Server databases—all within the comfort of their notebook environment.
– by Elizabeth on July 28, 2022
Bridging the exploratory analytics gap with Deepnote for Snowflake
A story of SQL-first notebooks, Deepnote—now a Snowflake Select Technology partner—and how Deepnote and Snowflake work together to power your SQL + Python workflows.
– by Harry on July 7, 2022
Deploy ML models to Snowflake with Deepnote & Modelbit
In this post, we’ll walk you through building and training a machine learning model in Deepnote and how to deploy it to Snowflake using Modelbit.
– by Lukas on June 30, 2022
Bringing ClickHouse performance to the comfort of data science notebooks
With Deepnote and ClickHouse, data teams can efficiently query very large datasets, extract relevant data, and start analyzing and modeling data—all within the comfort of their notebook environment.
– by Allan on June 14, 2022
Snowpark for Python: Bring Python to your data warehouse with Deepnote
What makes Snowpark such a valuable asset for data science workflows, and how you can use its superpowers in Deepnote—Snowpark’s Python-ready partner
– by Jakub on May 26, 2022
Deepnote is out of beta!
We’re removing the “beta” label and making Deepnote generally available for the world’s best data teams.
– by Filip on April 14, 2022
Welcome to Deepnote workspaces
We are excited to present Deepnote workspaces, a collaborative space for data teams of all sizes. A workspace is your data home and it helps you effectively organize and surface data projects, notebooks, and apps in one place, growing with you as your team and knowledge scales.
– by Simon on March 7, 2022
Profiling Node.js blocked event loop in production
How we set up just-in-time profiling of our nodejs app when its event loop gets stuck.
– by Allan on February 15, 2022
SQL just got machine learning
It's almost a given that the brightest tools in machine learning are written for Python. However, those with the deepest understanding of company data often speak SQL. Imagine what they could do if machine learning was at their fingertips—not in a Python environment but in the data layer—where they're most effective.
– by Jakub on January 31, 2022
Deepnote raises a $20M series A led by Accel & Index
Deepnote is quickly becoming a new standard for working with data. Today, we are announcing a $20M Series A led by Accel and Index. So why are the world’s best VCs doubling down on our vision? Because we are building a new kind of data science notebook.
– by Allan on January 6, 2022
How not to draw an owl
Chances are you've experienced the initial excitement of learning something new followed quickly by a stark realization — you are not familiar with the prerequisites. You've actually got a mountain to climb! This article uses Great Expectations to highlight how Deepnote naturally lends itself to effective, context-based learning.
– by Elizabeth on December 31, 2021
Deepnote's year in review: 2021
It’s the last day of the year! Here’s a recap of how the Deepnote team, community and product evolved in 2021. We wish you happy holidays, and a great start to 2022!
– by Robert on November 29, 2021
Product-led growth CRM in Notion
Drive your product-led growth from Notion, and use Deepnote to keep your CRM up to date. Add new entries to your database, and enrich the existing ones using scheduled notebooks.
– by Elizabeth on November 2, 2021
Bringing analytics to Notion with Deepnote
How to build charts over Notion databases and keep your analytics & storytelling all in one place
– by Elizabeth on September 1, 2021
Deepnote & Webex light up Times Square
Throughout September 2021, you can find Deepnote on one of New York's biggest screens - the NASDAQ Tower in Times Square as a part of the Webex App Hub campaign.
– by Jakub on June 15, 2021
Data science beyond data science teams
Today, we’re launching Deepnote for Teams. And going forward, we will be building even more features that allow anyone in any organization (not just data scientists) to have a seat at the table.
– by Jan on May 4, 2021
Survey of storage in data science notebook platforms in 2021
We looked at a couple of popular notebook platforms and compared their storage solutions both in managed and on-premise versions.
– by Filip on February 25, 2021
Building a design system at a startup
How to build a design system at a fast-moving startup and still leverage all the benefits a design system offers.
– by Jakub on February 6, 2020
Deepnote emerges from stealth with YC, Index, & Accel leading our seed round
We’ve been working behind the scenes for the past year. Today, we’re excited to announce that we raised $3.8M from YC, Index and Accel to build a new kind of data science notebook. Here’s our story.
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