You're working on a complex data engineering project. What tools do you need to collaborate virtually?

1 Version control

Version control is a system that tracks and manages changes to your code, data, and configuration files. It allows you to create, review, and merge branches, as well as revert, restore, and compare versions. Version control is essential for data engineering projects, as it enables you to collaborate with other developers, maintain code quality and consistency, and avoid conflicts and errors. Some of the popular version control tools for data engineering are Git, GitHub, GitLab, Bitbucket, and SVN.

4 Collaboration tools

Collaboration tools are applications that facilitate communication, coordination, and cooperation among team members. Collaboration tools can help you enhance your data engineering projects, as they enable you to share ideas, feedback, and documents, as well as to plan, track, and execute your tasks. You can use collaboration tools to chat, call, video conference, and screen share with your colleagues, clients, and managers. Some of the common collaboration tools for data engineering are Slack, Zoom, Google Meet, and Trello.

5 Documentation tools

Documentation tools are software that help you create, edit, and publish documents that describe your data engineering projects. Documentation tools can help you improve your data engineering projects, as they allow you to document your code, data, and processes, as well as to generate reports, diagrams, and dashboards. You can use documentation tools to explain your data engineering logic, methods, and results, as well as to visualize and communicate your data insights. Some of the popular documentation tools for data engineering are Jupyter Notebook, Sphinx, Markdown, and Tableau.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?