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DataForge Training Series

The DataForge team created a series of training videos to cover various aspects of the platform. We have grouped the training videos into three levels: Foundational, Intermediate, and Advanced.

If there are additional trainings you would find useful, please feel free to submit a request to our support team!

Foundational Level

You have a basic understanding of the core components and architecture of DataForge. You have a basic understanding of best practices for DataForge configuration. You understand how data flows between inputs to outputs and how transformations are defined.

Achievements:

  • Understand core components and high-level architecture of DataForge
  • Initial understanding of best practice for DataForge config
  • Understanding of data transformations
  • Difference between inputs and outputs

Resources:

Intermediate Level

You know enough about DataForge to develop complex solutions and custom features. You can contribute to architecture decisions when planning for development in DataForge. You understand the underlying data processing steps at a deeper level. You can browse existing environments and have a strong grasp of what was built out. You understand how extensibility works and how to build advanced configurations.

Achievements:

  • Understanding of inputs that impact architecture decisions
  • Deeper understanding of data processing steps in DataForge
  • Comfort with DataForge UI navigation
  • Advanced configuration knowledge

Resources:

Advanced Level

You have extensive mastery of DataForge . You understand how to deploy and manage DataForge. You know how to clone components, migrate configurations between environments, and set up custom compute clusters for processes. You know how the underlying physical architecture functions and can speak in-depth to how it works as part of a cloud-based infrastructure.

Achievements:

  • Understanding of core deployment concepts
  • Can have a conversation about various DataForge components
  • Knowledge of clusters and compute processes
  • High-level understanding of physical architecture

Resources: