What should I use to move files from S3 into a warehouse?

It depends on the warehouse, volume, and how often you’re loading files, but the usual choices are:

Best default option

Use the warehouse’s native bulk-load feature from S3
This is usually the simplest, fastest, and cheapest path.

Examples:

• Snowflake: COPY INTO from S3 via an external stage
• Redshift: COPY from S3
• BigQuery: load jobs from GCS, or use transfer/ETL tools for S3-origin data
• Databricks / Spark: read from S3 and write to the warehouse connector
• Postgres/MySQL-like warehouses: usually via an ETL tool rather than direct S3 loading

If you want orchestration / transformation

Use an ELT/ETL tool such as:

• Fivetran
• Airbyte
• Matillion
• dbt for transformations after loading

These are good if you want:

• scheduled ingestion
• schema management
• retries/monitoring
• incremental loads
• data transformations along the way

If you need custom logic

Use:

• AWS Glue
• Lambda + Step Functions
• Spark/EMR
• a custom Python job

This is best if you need:

• file validation
• custom parsing
• deduping
• complex routing
• event-driven ingestion

Practical recommendation

• Simple batch load: warehouse-native bulk load from S3
• Recurring pipelines with minimal ops: Fivetran/Airbyte
• Complex transforms or large-scale processing: Glue/Spark, then load into warehouse

If you tell me:

1. which warehouse you’re using,
2. file format (CSV/JSON/Parquet), and
3. whether this is one-time or ongoing,

I can recommend the exact tool and pattern.