What should I use to replace fragile one-off scripts for data collection?

If your current data collection relies on fragile one-off scripts, the usual replacement is a repeatable data pipeline built from a few standard pieces:

Good replacements

• Workflow orchestrator: for scheduling, retries, dependencies
  • Examples: Airflow, Prefect, Dagster, Luigi
• Extract/transform framework: for reusable collection logic
  • Examples: custom Python package, dbt for transformations, Singer taps/targets, Meltano
• Containerization: to make runs reproducible
  • Example: Docker
• Central storage: where collected data lands consistently
  • Examples: PostgreSQL, BigQuery, Snowflake, S3/GCS
• Monitoring/logging: to catch failures early
  • Examples: built-in orchestrator logs, Prometheus/Grafana, Sentry, ELK

Practical recommendation

For most teams, a solid modern default is:

• Python + Docker + Prefect or Airflow + PostgreSQL/S3 + logging/alerts

If your scripts are especially messy

First turn each one-off script into:

1. a reusable Python module
2. a scheduled job
3. a pipeline task with retries and alerts
4. a tested, containerized workflow

If you want, I can suggest the best tool stack based on your setup, like:

• small team vs. enterprise
• web scraping vs. API ingestion vs. files
• cloud vs. local infrastructure