Next steps - GitHub Tutorial

Contents

• Introduction Introduction

  • Data pipeline automation with GitHub Actions

    46s
  • What you should know

    39s
• 1. EIA API 1. EIA API

  • EIA API

    2m 47s
  • Setting an environment variable

    3m 22s
  • The EIA API dashboard

    4m 10s
  • GET request structure

    5m 41s
  • Querying the data via the browser

    4m 4s
  • Querying data with R and Python

    2m 50s
  • Pulling metadata from API with R

    3m 5s
  • Sending a simple GET request with R

    5m 19s
  • API limitations with R

    4m 43s
  • Handling a large data request with R

    4m 27s
  • Pulling metadata from API with Python

    3m 47s
  • Sending a simple GET request with Python

    4m 44s
  • API limitations with Python

    3m 54s
  • Handling a large data request with Python

    3m 10s
  • Challenge: Query the API

    1m 2s
  • Solution: Query the API with R

    7m 28s
  • Solution: Query the API with Python

    7m 45s
• 2. Data Automation 2. Data Automation

  • Data pipeline scope and requirements

    3m 31s
  • Data pipeline architecture

    2m 19s
  • Data refresh process

    4m 28s
  • ETL supporting functions

    2m 16s
  • Data backfilling

    5m 54s
  • Data refresh output

    4m 12s
  • Data quality checks

    3m 7s
• 3. Deployment 3. Deployment

  • Introduction to GitHub Actions

    3m 32s
  • Deployment with Docker

    1m 38s
  • Setting GitHub Actions workflow

    9m 42s
  • Reviewing workflows logs

    2m 5s
  • Setting secrets and environment variables

    1m 44s
  • Advanced workflow

    4m 42s
  • Data pipeline deployment

    3m 11s
• 4. Monitoring 4. Monitoring

  • Data pipeline maintenance

    2m 44s
  • Deploying dashboard to GitHub Pages

    3m 19s
• Conclusion Conclusion

  • Next steps

    15s