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By Megan on December 5, 2023
How to use Jupyter notebooks in 2024
An overview of how data scientists are using Jupyter notebooks online for real time collaboration in 2024.
– by Nick on November 29, 2023
Connecting to Amazon Redshift in 2024
A brief overview of how to connect to your AWS Redshift with Python in 2024
– by Nick on November 29, 2023
Connecting to Google BigQuery in 2024
A brief overview of how to connect to your BigQuery with Python in 2024
– by Nick on November 29, 2023
Connecting to Snowflake in 2024
A brief overview of how to connect to your Snowflake with Python in 2024
By Deepnote team on November 14, 2023
DLT & Deepnote in women's wellness and violence trends: A Visual Analysis
Hiba Jamal from dlthub combines dlt and Deepnote to explore trends in women's wellness and violence, where dlt formatted her deeply nested datasets into my desired destination with minimal supervision and Deepnote acted as an interactive dashboarding tool.
– by Deepnote team on November 2, 2023
Noteable vs Deepnote: a side-by-side comparison for 2023
A comparison between popular data science tools Deepnote and Noteable.
By Gabor on November 1, 2023
Deepnote Auto AI vs. ChatGPT: Autonomous Analysis Showdown
Comparing Deepnote Auto AI and ChatGPT's Advanced Analysis plugin, we explore how each tool enhances data analysis, weighing their capabilities, collaboration features, and overall integration into professional workflows.
By Filip on October 27, 2023
Finding a Redash alternative: An intro to Deepnote
Discover how Deepnote's unique blend of SQL, Python, and AI capabilities offers a versatile and powerful alternative to Redash for your data analysis needs.
By Megan on October 26, 2023
Revolutionizing data work with Deepnote AI - again
If you thought we were done reshaping data workflows with AI, we’ve been working on one more addition to the Deepnote AI family. And guess what? It’s the best one yet.
By Megan on October 17, 2023
IPYNB to PDF: The easiest way to export your Jupyter notebooks
Need to turn your Jupyter notebook into a PDF? Then you probably need a new game plan. See why it’s time to hop off the .ipynb to PDF merry-go-round.
By Deepnote team on October 9, 2023
8 Data notebook myths - busted
Some data notebook myths aren’t just wrong — they also prevent data teams from doing their best work. Learn why it’s time to scrap these common misconceptions.
By Deepnote team on October 6, 2023
Disrupting Fintech: How Floryn reimagined the lending process using Deepnote
Within the fast-moving and competitive field of Fintech, Floryn has managed to position itself as a forward-thinking disruptor in the field.
By Ondřej on October 4, 2023
Behind Deepnote AI Copilot
Recently, Deepnote became the first data notebook with an integrated AI Copilot. It offers blazingly fast code suggestions as you type, letting you write code more quickly and with fewer errors. Let’s explore how Deepnote AI Copilot works behind the scenes.
By Deepnote team on September 29, 2023
Experience the possibilities of Deepnote through the Github Student Developer Pack
The Github Student Developer Pack gives students hands-on experience with data science and machine learning projects through its partnership with Deepnote.
By Gabor on September 18, 2023
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.
By Jakub on September 12, 2023
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.
By Deepnote team on August 30, 2023
Deepnote Achieves Google Cloud Ready - AlloyDB
Deepnote is proud to announce AlloyDB Ready designation.
By Deepnote team on August 30, 2023
Deepnote Achieves Google Cloud Ready - Cloud SQL Designation
Deepnote is proud to announce Cloud SQL Ready designation.
By Jakub on August 28, 2023
Deepnote Achieves Google Cloud Ready - BigQuery Designation
Deepnote is proud to announce it's designation as Google Cloud Ready - BigQuery
By Deepnote team on August 1, 2023
Using Deepnote AI & Modelbit to build an ML model and deploy it to production
By Gabor on July 31, 2023
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.
By Gabor on June 20, 2023
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.
By Deepnote team on May 15, 2023
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.
– by Eric on April 17, 2023
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.
– by Eric on April 10, 2023
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.
– by Eric on April 3, 2023
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.
– by Robert on March 30, 2023
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.
– by Christopher on March 27, 2023
From spreadsheets to notebooks: the BizOps playbook
BizOps is all about optimization. Learn how to improve your analytical workflows by replacing spreadsheets with notebooks.
– by Eric on March 20, 2023
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.
– by Eric on March 13, 2023
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.
– by Eric on March 7, 2023
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.
– by Jakub on March 6, 2023
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.
– by Avi on February 27, 2023
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.
– by Eric on February 20, 2023
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.
– by Eric on February 7, 2023
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.
– by Eric on February 6, 2023
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.
– by Gabor on February 2, 2023
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.
– by Avi on January 31, 2023
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.
– 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 Jakub 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
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.
– by Gabor on November 16, 2022
Data visualization for everyone: Meet the new chart block
See how we redesigned our no-code charting experience to help streamline data visualization.
– by Jakub on November 15, 2022
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.
– by Mark on November 11, 2022
Tutorial: filtering with pandas
Learn how to leverage the Python pandas framework to filter data for a wide range of use cases.
– by Jakub on November 2, 2022
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.
– by Elizabeth on October 18, 2022
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.
– by Gabor on September 15, 2022
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.
– by Lukas on August 11, 2022
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.
– by Elizabeth on July 28, 2022
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.
– by Harry on July 7, 2022
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.
– by Lukas on June 30, 2022
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.
– by Allan on June 14, 2022
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.
– 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're excited to present Deepnote workspaces — a collaborative space for data teams of all sizes to organize, share, and scale knowledge.
– by Allan on February 15, 2022
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.
– by Jakub on January 31, 2022
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.
– by Allan on January 6, 2022
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.
– by Elizabeth on December 31, 2021
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!
– by Robert on November 29, 2021
Guide: how to create a product-led growth CRM in Notion
Learn how to drive your product-led growth from Notion and use Deepnote to keep your CRM up to date.
– by Elizabeth on November 2, 2021
Bringing analytics to Notion with Deepnote
Learn 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
See Deepnote on one of New York's biggest screens — the NASDAQ Tower in Times Square — as part of the Webex App Hub campaign.
– by Jakub on June 15, 2021
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.
– by Jan on May 4, 2021
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.
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