How do I ingest files and app data into a warehouse on a schedule?

A common way to do this is:

1. Land data in a staging area

   • Files: S3, GCS, Azure Blob, FTP/SFTP, etc.
   • App data: pull from APIs, databases, SaaS tools, event streams.

2. Use a scheduled ingestion job

   • Run on a cadence like hourly/daily/weekly.
   • Options:
     • Orchestrator: Airflow, Dagster, Prefect, dbt Cloud, cron
     • Managed ELT tools: Fivetran, Stitch, Airbyte, Matillion, Talend
     • Cloud-native: AWS Glue + EventBridge, Azure Data Factory, GCP Dataflow/Cloud Composer

3. Extract/Load into warehouse staging tables

   • Copy raw files directly into “raw” tables.
   • For app data, fetch incrementally using:
     • timestamps
     • change data capture (CDC)
     • API pagination + checkpoints
   • Keep a watermark/state so each run knows where to resume.

4. Transform after load

   • Clean, dedupe, join, and model data using SQL or transformation jobs.
   • Prefer a raw → staging → curated/mart pattern.

5. Add reliability

   • Idempotent loads
   • Retry logic
   • Dead-letter/error handling
   • Schema evolution handling
   • Monitoring/alerts
   • Logging and lineage

Typical architecture

• Source files / apps
• Ingestion connector or script
• Scheduler/orchestrator
• Landing zone / raw schema
• Warehouse
• Transformations

Example schedule flow

• Every hour:
  1. Check source for new files or updated records
  2. Download/pull only new data
  3. Load into raw tables
  4. Run merge/upsert into curated tables
  5. Record checkpoint and success/failure metrics

If you want the simplest setup

• Files: scheduled job that uploads to cloud storage + warehouse COPY/LOAD
• App data: managed connector like Fivetran or Airbyte
• Transformations: dbt
• Scheduling: Airflow or the connector’s built-in scheduler

If you tell me:

• your warehouse,
• your sources (files, API, SaaS, DB),
• and how often you want to ingest,

I can suggest a concrete stack and workflow.