DataBurst

We are a group of dedicated data engineers at DataBurst, excited to share comprehensive roadmaps tailored for data engineering professionals at all levels—from those just starting out to the most seasoned experts.

Whether you're a junior engineer seeking direction or a senior professional aiming to stay ahead of the curve, these roadmaps offer valuable guidance and industry best practices.

It's not an end; these roadmaps are living documents. If you have suggestions, want to make changes, or add new insights, we welcome your contributions. Join our collaborative effort to refine and enhance these roadmaps for the data engineering community.

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Website: www.databurst.tech

Discord: DataBurst Discord

Table of Contents

• DataBurst
  • Table of Contents
  • Data Engineering Roadmap
  • Cloud Data Engineering Roadmap
  • Roadmap Guide
  • DataBurst Wiki
  • DataBurst Team (Alphabetical Order)
  • Contributors
  • Contributing
  • License
  • Star-history

Data Engineering Roadmap

Cloud Data Engineering Roadmap

We are thrilled to introduce the Cloud Data Engineering Roadmap, which builds on the foundation of our existing Data Engineering Roadmap. It follows the same start-to-end structure with a key distinction: whenever a tool is introduced in the traditional roadmap, its cloud-based counterparts focused on AWS, Azure, and GCP are introduced here.

For sure, all tools mentioned in the Data Engineering Roadmap can also be utilized in cloud data engineering scenarios. However, to avoid duplication and reduce confusion, we have preferred to focus solely on cloud-based tools in the Cloud Data Engineering Roadmap.

This roadmap is designed to cater to data professionals aiming to specialize in cloud data engineering, helping them leverage the latest cloud technologies and practices.

Roadmap Guide

For a detailed guide on how to navigate and utilize the Roadmaps, please refer to our Roadmap Guide.

DataBurst Wiki

The DataBurst Wiki is a key resource designed to complement the Data Engineering Roadmaps. It provides a brief overview of each topic covered in the roadmaps and introduces free resources to help you dive deeper into each subject.

The DataBurst Wiki serves as a high-level introduction to various key areas in data engineering, and for each topic, we also provide a curated list of free resources to help you dive deeper. These resources may include:

• Relevant articles and books
• Tutorial links
• Online courses and documentation

This wiki is continuously updated and improved, and we encourage community contributions to ensure it stays relevant and useful for learners at all levels.

To contribute or explore the wiki further, visit the GitHub Wiki Repository.

We hope you find it helpful in your data engineering journey!

DataBurst Team (Alphabetical Order)

• Alireza Shateri
• Dorsa Hasanlee
• Mostafa Ghadimi
• Niyusha Baghayi
• Reza Amini Majd

Contributors

Alireza Khorami	Alireza Shateri	Amirreza Aflakparast	Dorsa Hasanlee	Farid Arab	Mostafa Ghadimi	Niyusha Baghayi
Rez	mahdi alizadeh

And more! We are expecting more passionate contributors and data lovers to join us...

Contributing

We welcome contributions to our data engineering roadmaps! If you'd like to get involved, here's how you can help:

• Fork the data-burst/data-engineering-roadmap repository
• Modify the roadmap.yaml file based on your suggestions and ideas for improving the Data Engineering Roadmap
• Modify the cloud-roadmap.yaml file for suggestions and improvements related to the Cloud Data Engineering Roadmap
• Once you've made your changes, commit them and submit a pull request

For more details on our contribution guidelines, please check out the CONTIRIBUTION.md file in the repository. We're excited to collaborate with the community and make these roadmaps even better. Looking forward to your contributions!

License

This repository is licensed under the MIT License, which is a permissive open-source license that allows for reuse and modification of the code with few restrictions. You can find the full text of the license in this file.

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