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Madbax

By Udemy · Official page

Analytics Engineering: Build an End-to-End ELT Data Pipeline

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Analytics Engineering: Build an End-to-End ELT Data Pipeline

Course Curriculum

  • 8 sections • 22 lectures • 3h 34m total length
  • Introduction to Faker pandas DuckDB and dbt 10:36
  • Generate the Seed Dataset 19:12

Compatibility & Requirements

  • Load Raw CSVs into dbt Seeds 8:25
  • Build Staging Models (Silver Layer)11:46
  • Build Business Marts (Gold Layer)13:15
  • Add Automated dbt Tests 15:56
  • Installing Git 4:35
  • Git Configuration 2:24
  • Create a Github Account 3:19
  • Create a gitignore file 7:01
  • Project Architecture Diagrams 10:24
  • Create a README File 23:09
  • Publish Your Analytics Project to GitHub 18:37
  • No prior Analytics Engineering or Data Engineering experience is required.
  • Basic familiarity with computers and working with files and folders is helpful.
  • Basic Python or SQL knowledge is helpful, but not essential, as the project is explained step by step
  • A Windows, macOS, or Linux computer capable of running Python and the project tools.
  • An internet connection for downloading and installing the required software and packages.
  • A code editor such as Visual Studio Code.
  • A free GitHub account for publishing the completed portfolio project.
  • A willingness to learn by building a complete hands-on project from start to finish.

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What is included in the download archive?

The archive includes all project and video files for this release. It does not include one-to-one mentoring or certificates from the original school.

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