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In every profession, people tend to focus most of their energy and resources on the actual execution of the task. An aspect that is most of the time neglected and left for the last minute, is the explanation of the project to its audience. That is when documentation comes to the forefront.

Proper documentation is a critical aspect of any software development project, including Python-Snowflake projects. Documentation not only helps developers and readers of the code understand the project. It also helps stakeholders and users to make informed decisions.

The practices

Here are five best practices that I strongly recommend for the proper documentation of your Python-Snowflake project:

  • Write clear and concise documentation: The primary goal of documentation is to make it easy for others to understand your project. Therefore, it’s essential to write clear and concise documentation that avoids technical jargon and is easy to follow. Use simple language and provide examples to explain any complex concepts.
  • Use version control: Version control is crucial for software development projects, including Python-Snowflake projects. It helps to keep track of changes made to the code and documentation. That means that collaboration with other developers is easier. Using tools like Git and GitHub can ensure that your project is well-documented and up-to-date.
  • Include a README file: The README file is often the first thing that developers and users will see when accessing your project. Therefore, it’s crucial to include essential information about your project. Such as its purpose, how to install and run it, and any dependencies or requirements.
    • The first part could be a short and concise summary-description of the project and its scope. The next section should be the dependencies section, which lists the requirements for executing the code. Any APIs or datasets that are used as inputs in the program should be mentioned separately and it should also include the APIs url links. Finally, it should include an explanation of the code. This section should cover all the functions running in the back end of the project, the connection of the code with the data cloud platform (in this case, Snowflake), and the description of tables and their relations.
  • Use consistent formatting and organization: Consistency in formatting and organization can make it easier for developers to navigate your project’s documentation. Use headings and subheadings to break up the content and use the same formatting and styles throughout the documentation.
  • Keep your documentation up-to-date: Documentation can quickly become outdated if not properly maintained. Therefore, it’s essential to keep your documentation up-to-date as your project evolves. Make it a habit to update your documentation regularly, especially after making significant changes to your project.

Case studies

As an example a project that involves building an API to access Snowflake data. In this case, your documentation should include details about the API endpoints, how to authenticate and authorize requests, and any specific requirements or limitations for the API.

Conclusion

In conclusion, proper documentation is critical for Python-Snowflake projects. By following these best practices, you can ensure that your documentation is clear, consistent, and up-to-date, making it easier for developers and users to understand and use your project.

Auteur

  • Dimitris Lampriadis

    Dimitris holds a Master's degree as an Urban Technologist - Geospatial Data Analyst from MaCT/UPC in Barcelona. There he discovered a passion for geospatial data analysis and Urban Analytics, but most importantly how data-driven strategies are crucial for making efficient decisions, and how to translate raw data into handy and easy-to-read insights. With his sights set on becoming an Analytics Engineer, Dimitris sought out a company that would help him grow his skills and knowledge. That's when he found Nimbus Intelligence. Nimbus provided the necessary space to combine his existing knowledge and skills, with a suite of powerful tools, like; Snowflake, Tableau, Alteryx, and dbt. He's also managed to develop valuable business skills that enable him to solve complex problems and deliver tangible results. He enjoys giving answers to modern business problems and aims to bring knowledge, to every team he is part of, that binds his understanding of spatial relationships and Analytics Engineering. He is always looking for ways to share his expertise and collaborate with others.

Dimitris Lampriadis

Dimitris holds a Master's degree as an Urban Technologist - Geospatial Data Analyst from MaCT/UPC in Barcelona. There he discovered a passion for geospatial data analysis and Urban Analytics, but most importantly how data-driven strategies are crucial for making efficient decisions, and how to translate raw data into handy and easy-to-read insights. With his sights set on becoming an Analytics Engineer, Dimitris sought out a company that would help him grow his skills and knowledge. That's when he found Nimbus Intelligence. Nimbus provided the necessary space to combine his existing knowledge and skills, with a suite of powerful tools, like; Snowflake, Tableau, Alteryx, and dbt. He's also managed to develop valuable business skills that enable him to solve complex problems and deliver tangible results. He enjoys giving answers to modern business problems and aims to bring knowledge, to every team he is part of, that binds his understanding of spatial relationships and Analytics Engineering. He is always looking for ways to share his expertise and collaborate with others.