Instead of Jupyter Notebook: Benefits of Deepnote Jupyter note | Big Data Science

Instead of Jupyter Notebook: Benefits of Deepnote
Jupyter notebooks have been actively using data analytics for many years and contain ML. However, despite its popularity, this research tool has significant implications:
• Difficulty in code versioning. Browsing Jupyter notebooks comes in the form of large JSON files, merging two notebooks is next to impossible. As is the usual use by developers of a Git-like tool version.
• Lack of high efficiency with IDE, code highlighting and tooltips. Usually a Data Scientist is not a professional software developer, and therefore tools that regulate the quality of the code and the value of its increase are very important.
• Difficulties of development through testing. The popular test-driven development methodology (test-driven development) is almost impossible to implement in Jupyter notebooks. Therefore, they cannot be used in data pipelines.
• Non-linear workflow due to transition from one cell to another. This may seem like an irreproducible experiment. The interactive way to encode and navigate between cells is both one of Jupyter Notebook's best features and its biggest weakness.
• Jupyter is not well suited for running long running asynchronous data scoping tasks.

Many of the shortcomings are addressed as an alternative to Jupyter Notebook called Deepnote. Deepnote, like Jupyter, is an interactive notebook for solving a DS problem, but it outperforms its competitor in a number of advantages :
• Real-time Collaboration - Created by Google Docs, you can share links to your notebook with colleagues, giving everyone the desired level of access (view, execute, comment, edit and full control). In addition, a cell in Deepnote allows a collaborator to leave comments, eliminating the need to switch between applications for posting messages and code for providing feedback. With access to the developer's code, managers and other board members easily follow the code development progress and development life cycle.
• Easy environment management deployment - Deepnote takes the job of installing modules and setting up the environment to run Python, including versions. In addition to Python, Deepnote also supports SQL queries.
• Deepnote has the ability to embed blocks of code in blogs and other repositories by implementing the creation of a GitHub project specifically for this purpose. Deepnote Cells allows you to embed code only, embed output only, and embed both code and output.
• Data Visualisation - Jupyter notebooks almost never evaluate EDA execution without explicit coding. Deepnote provides an early development tool in the notebook itself - the early block allows you to generate information, just like building Python, but without the need to write code.
• Save time and money - once Deepnote is in charge of code management and processing, teams don't need to commit their code pipelines to tools like GitHub, BitBucket, etc. thus reducing operating costs.
Try it for free: https://deepnote.com/