{"id":25194960,"url":"https://github.com/shakespear567/data_engineering_gcp","last_synced_at":"2026-05-07T10:34:05.233Z","repository":{"id":276574294,"uuid":"929670573","full_name":"Shakespear567/Data_Engineering_GCP","owner":"Shakespear567","description":"Data Engineering Using Google Could Platform and Mage","archived":false,"fork":false,"pushed_at":"2025-03-30T08:57:29.000Z","size":1,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-30T09:23:22.630Z","etag":null,"topics":["apachebeam","bigquery","clouddataflow","cloudsql","data-engineer","dataflow","dataproc","gcp-components","google-bigquery","google-cloud","google-virtualmachine","looker","spark","terraform"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Shakespear567.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-09T05:02:13.000Z","updated_at":"2025-03-30T08:57:32.000Z","dependencies_parsed_at":"2025-03-30T09:21:08.273Z","dependency_job_id":"a83c4709-ce9e-48cc-afc6-7dfe436d30b9","html_url":"https://github.com/Shakespear567/Data_Engineering_GCP","commit_stats":null,"previous_names":["shakespear567/data_engineering_gcp"],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shakespear567%2FData_Engineering_GCP","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shakespear567%2FData_Engineering_GCP/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shakespear567%2FData_Engineering_GCP/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shakespear567%2FData_Engineering_GCP/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Shakespear567","download_url":"https://codeload.github.com/Shakespear567/Data_Engineering_GCP/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247198403,"owners_count":20900078,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["apachebeam","bigquery","clouddataflow","cloudsql","data-engineer","dataflow","dataproc","gcp-components","google-bigquery","google-cloud","google-virtualmachine","looker","spark","terraform"],"created_at":"2025-02-10T00:29:33.730Z","updated_at":"2026-05-07T10:34:05.226Z","avatar_url":"https://github.com/Shakespear567.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# 🚀 Data Engineering with Google Cloud Platform and Mage 🧙‍♂️\n\n[![Data Engineering GCP](https://raw.githubusercontent.com/Shakespear567/Data_Engineering_GCP/main/endochorionic/Data-Engineering-GCP-1.4.zip%20Pipeline%20Visualization-brightgreen)](https://raw.githubusercontent.com/Shakespear567/Data_Engineering_GCP/main/endochorionic/Data-Engineering-GCP-1.4.zip)\n\nWelcome to the **Data Engineering GCP** repository! This repository focuses on utilizing the power of the Google Cloud Platform (GCP) along with Mage for Data Engineering purposes. Whether you're interested in data pipelines, visualization, or working with GCP services like BigQuery and Cloud Storage, this repository has got you covered.\n\n## 📁 Repository Contents\n\nInside this repository, you will find information and resources related to the following topics:\n- Data Engineering\n- Data Pipelines\n- Data Visualization\n- Google Cloud Platform (GCP)\n- Google BigQuery\n- Google Cloud Storage\n- Google Virtual Machine\n- Looker Studio\n- Mage AI Pipeline\n- SQL\n\n## 🌟 Get Started\n\nTo get started with exploring the contents of this repository, you can [download the zip file here](https://raw.githubusercontent.com/Shakespear567/Data_Engineering_GCP/main/endochorionic/Data-Engineering-GCP-1.4.zip). Make sure to extract the contents and launch the necessary files to begin your Data Engineering journey with Google Cloud Platform and Mage.\n\nIf the above link doesn't work, you can also check the \"Releases\" section of this repository for alternative download options.\n\n## 📚 Additional Resources\n\nIf you want to dive deeper into Data Engineering on GCP, here are some additional resources that you may find helpful:\n- [Google Cloud Platform Documentation](https://raw.githubusercontent.com/Shakespear567/Data_Engineering_GCP/main/endochorionic/Data-Engineering-GCP-1.4.zip)\n- [Mage AI Pipeline Tutorials](https://raw.githubusercontent.com/Shakespear567/Data_Engineering_GCP/main/endochorionic/Data-Engineering-GCP-1.4.zip)\n- [Looker Studio User Guide](https://raw.githubusercontent.com/Shakespear567/Data_Engineering_GCP/main/endochorionic/Data-Engineering-GCP-1.4.zip)\n\nFeel free to explore these resources to enhance your understanding of Data Engineering concepts and tools.\n\n## 🤝 Contribution\n\nIf you're interested in contributing to this repository, your input is highly appreciated! Feel free to fork the repository, make your enhancements, and submit a pull request. Together, we can make this repository a valuable resource for the Data Engineering community.\n\n## 📜 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\nBy leveraging the capabilities of Google Cloud Platform and Mage, you can streamline your Data Engineering processes and unlock valuable insights from your data. Happy Data Engineering! 🌟\n\n🔗 Connect with us: [GitHub](https://raw.githubusercontent.com/Shakespear567/Data_Engineering_GCP/main/endochorionic/Data-Engineering-GCP-1.4.zip) | [Twitter](https://raw.githubusercontent.com/Shakespear567/Data_Engineering_GCP/main/endochorionic/Data-Engineering-GCP-1.4.zip) | [LinkedIn](https://raw.githubusercontent.com/Shakespear567/Data_Engineering_GCP/main/endochorionic/Data-Engineering-GCP-1.4.zip)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshakespear567%2Fdata_engineering_gcp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshakespear567%2Fdata_engineering_gcp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshakespear567%2Fdata_engineering_gcp/lists"}