#workflow

Design a production-grade Airflow DAG for daily ETL. Workflow: 1. Extract data from PostgreSQL and REST API. 2. Transform using pandas (clean, join, aggregate). 3. Load to data warehouse (Snowflake/BigQuery). 4. Send Slack notification on success/failure. 5. Implement retry logic and SLA monitoring. Use TaskGroups for organization, XComs for data passing, and proper error handling with callbacks.

Visualize a team of AI agents working together. The 'Crew' includes: 1. 'Researcher' (fetches facts). 2. 'Writer' (drafts blog). 3. 'Manager' (approves/feedback). Show a timeline of tasks being handed off between agents. Use a clean, modern dashboard with status badges (Ongoing, Completed, Failed).

Master Git for effective collaboration. Workflow: 1. Feature branches (git checkout -b feature/new-feature). 2. Commit often with clear messages (feat:, fix:, docs:). 3. Pull before push (git pull --rebase). 4. Code review via pull requests. 5. Squash commits before merge. 6. Delete merged branches. 7. Tag releases (v1.0.0). Commit message format: type(scope): subject. Use .gitignore. Never commit secrets. Interactive rebase for clean history (git rebase -i). Resolve conflicts carefully. Use git stash for WIP. Learn: git log, git blame, git bisect. Branching strategies: Git Flow, GitHub Flow.

Choose Git workflow for team. Strategies: 1. Git Flow (main, develop, feature, release, hotfix). 2. GitHub Flow (main, feature branches, PR). 3. Trunk-based (short-lived branches, frequent merges). 4. Branch naming conventions. 5. Commit message standards. 6. Pull request templates. 7. Protected branches. 8. Squash vs merge commits. Use GitHub Actions or GitLab CI. Automate what you can.