Powerful SaaS integration toolkit for SaaS developers - create, amplify, manage and publish native integrations from within your app with Cyclr's flexible Embedded iPaaS.
sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Which is more popular?
Deepnote might be a bit more popular than
data.world.
We know about 34 links to it since March 2021
and only 24 links to data.world.
social mentions
34 vs 24
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
111 vs 59
Base details
Website, pricing, platforms and company facts side by side.
Collaborative Features Deepnote allows for real-time collaboration, similar to Google Docs, where multiple users can work on the same notebook simultaneously without conflicts.
Integration with Popular Tools Deepnote integrates seamlessly with popular data sources and tools such as Google Drive, GitHub, and SQL databases, enhancing its versatility for data science projects.
User-Friendly Interface The interface is clean and easy to navigate, making it accessible for both beginners and experienced data scientists.
Cloud-Based Being a cloud-based solution, Deepnote eliminates the need for local setup and maintenance, allowing users to access their projects from anywhere with internet access.
Data Security Deepnote provides robust security features, ensuring that your data and notebooks are protected against unauthorized access.
Integrated Version Control Version control within Deepnote allows users to track changes, revert to previous versions, and collaborate more effectively on shared projects.
Possible disadvantages
Limited Offline Access As a cloud-based platform, Deepnote requires an internet connection for most of its functionality, which can be a limitation for users needing offline access.
Performance Constraints Heavy computational tasks might be limited by the performance capabilities of the cloud resources provided, affecting users who require extensive computational power.
Subscription Costs While there is a free tier, advanced features and increased resource limits come at a subscription cost, which might be a consideration for students or hobbyists.
Learning Curve for Advanced Features While basic functionality is user-friendly, mastering the more advanced features and integrations may require a learning curve, especially for users new to data science tools.
Dependency on External Infrastructure The performance and availability of Deepnote can be affected by issues with their cloud service providers, which adds a layer of dependency on external infrastructure.
Collaborative Environment data.world provides a platform for teams to collaborate on data projects in real-time, making it easier for data scientists, analysts, and enthusiasts to work together and share insights.
Integration Capabilities The platform supports integrations with popular tools and services like Excel, Tableau, and Python, making it easier to import, export, and manipulate data across various applications.
Extensive Dataset Catalog data.world offers a vast collection of public datasets, empowering users to find and leverage data from a wide range of sources for their projects.
Querying Tools Users can execute SQL queries directly on the data.world platform, enabling powerful data analysis and transformations within the environment.
User-Friendly Interface The platform features an intuitive user interface that makes it accessible for users with varying levels of technical expertise.
Possible disadvantages
Pricing While data.world offers a free tier, more advanced features and functionality require a paid subscription, which might be cost-prohibitive for individuals or smaller organizations.
Learning Curve Despite its user-friendly interface, there is still a learning curve associated with fully utilizing all of the platform's features, particularly for users who are not familiar with SQL or data analysis tools.
Performance Limitations For very large datasets or complex analytical operations, the platform may experience performance constraints, potentially requiring users to rely on more powerful, external data processing tools.
Data Privacy Concerns As with any cloud-based platform, there are inherent data privacy and security concerns. Users must be cautious about the sensitivity of the data they upload and ensure compliance with relevant regulations.
Feature Parity with Competitors While data.world offers many great features, some users might find that other data collaboration platforms provide more advanced or specialized tools that better suit their needs.
Analysis
An editorial look at what each product does well and who it suits.
Deepnotedata.world
Overall verdict
Deepnote is an excellent tool for data scientists, particularly those who value collaboration and need interactive, shareable notebooks. Its user-friendly interface and powerful integration capabilities make it a strong contender in the data science notebook space.
Why this product is good
Deepnote is a collaborative data science notebook designed to enhance productivity and simplify the data science workflow. It offers real-time collaboration, similar to Google Docs, making it easier for teams to work together efficiently. It supports various programming languages and integrates seamlessly with popular tools such as Jupyter notebooks, Git, and cloud storage services. Deepnote also provides a strong focus on data visualization and interactive dashboards, making it easier to interpret and present data insights.
Recommended for
Data scientists who work in teams and need a collaborative environment.
Professionals who require seamless integration with existing tools and cloud storage.
Users who prioritize interactive data visualization and interpretability.
Educators looking for an accessible platform to teach data science concepts.
Overall verdict
Overall, data.world is regarded as a beneficial platform for data enthusiasts, professionals, and organizations looking to collaborate on data projects. Its user-friendly interface, strong community focus, and extensive features make it a valuable resource for those working with data.
Why this product is good
data.world is considered a good platform for a variety of reasons. It acts as a collaborative data community where users can discover and share open data. The platform provides tools for collaborative data projects, making it easier for users to work together on data analysis and insights. It also supports a wide range of data formats and offers integrations with other tools and platforms, enhancing its versatility. Additionally, data.world emphasizes openness and transparency, which can foster trust among users who are seeking reliable data sources.
Recommended for
Data analysts and scientists who need a collaborative environment to work on data projects.
Organizations looking to share and manage their data with a broader community.
Educators and researchers seeking open data sets for teaching or scholarly purposes.
Business professionals who require integration with other data tools for enhanced data insights.
Deepnote is a cloud-based data science notebook platform comparable to Jupyter Notebooks but with a focus on real-time collaboration and editing. It lets users write and run code in several programming languages, as...
Deepnote is a real-time collaborative notebook. It offers features like real-time collaboration, version control, and smart autocomplete. It also provides direct integrations with popular data sources like GitHub,...
Deepnote is a cloud-based, data science notebook platform that is similar to Jupyter Notebooks, but with a focus on collaboration and real-time editing. It allows users to write and execute code in a variety of...
Thank you for the list - I think I've come across all of these in my research! I'll try highlight the differences for each. - https://noteable.io/ - as you say, it doesn't exist anymore - https://deepnote.com - I actually mentioned this...
- Source: Hacker News
/
over 2 years ago
Deepnote - A new data science notebook. Jupyter is compatible with real-time collaboration and running in the cloud. The free tier includes unlimited personal projects, up to 750 hours of standard hardware, and teams with up to 3 editors.
- Source: dev.to
/
almost 3 years ago
I'll be sure to check out data.world propose to use it if it makes sense, thanks.
Source:
over 3 years ago
GIS data for a project. I apologize for the banality of my request and for my English.
Just google qgis datasets. There are so so many interesting sets you will find. Check out qgis.org, or data.world for starters.
Source:
over 3 years ago
Best way to open source a my dataset?
But, I'm also aware that there are dedicated platforms to catalog and share data (e.g. https://www.dolthub.com/, https://data.world/), and that uploading data on Github, in general, doesn't seem best practise.
Source:
over 3 years ago
ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.
Denodo delivers on-demand real-time data access to many sources as integrated data services with high performance using intelligent real-time query optimization, caching, in-memory and hybrid strategies.
Saturn Cloud
Amazon SageMaker
Databricks Unified Analytics Platform
Azure Synapse Analytics
Google BigQuery
GeoSpock
Delta Lake
Denodo
IBM Cloud Pak for Data
Teradata QueryGrid
K2View Fabric
Trustgrid Data Mesh Platform
Zetaris Platform
MarkLogic Data Hub Platform
