I'm building an ELT setup for warehouse loading, what should I use?

For warehouse loading in an ELT setup, the usual pattern is:

1. Extract data from source systems
2. Load raw data into your warehouse
3. Transform inside the warehouse

So what you should “use” depends on where the loading happens and your scale, but the common choices are:

If you mean tools for loading data into the warehouse

Use a batch ingestion / ELT loader such as:

• Fivetran
• Airbyte
• Stitch
• Matillion
• dbt for transformations after load
• Native warehouse loaders like:
  • Snowflake COPY INTO
  • BigQuery LOAD jobs
  • Redshift COPY
  • Databricks Auto Loader / COPY INTO

If you want a modern ELT stack

A typical stack is:

• Source connectors: Fivetran or Airbyte
• Warehouse: Snowflake / BigQuery / Redshift / Databricks
• Transforms: dbt
• Orchestration: Airflow / Dagster / Prefect

If “warehouse loading” means loading files into cloud storage first

Then use:

• S3 / GCS / Azure Blob as landing zone
• Then warehouse-native bulk load jobs or ingestion tools

Quick recommendation

If you want a simple, reliable setup:

• Airbyte + Snowflake + dbt or
• Fivetran + BigQuery + dbt

If you want, I can recommend the best option based on:

• your cloud provider,
• data volume,
• batch vs near-real-time,
• and budget.