Etlworks has dedicated flow types and end-to-end guides for each major cloud data warehouse. The patterns are the same across all of them — ETL, ELT, bulk load from cloud storage, reverse ETL — with platform-specific details (file formats, COPY command syntax, staging conventions) covered in each warehouse's Get started guide.
Per-warehouse guides
| Warehouse | Get started guide |
|---|---|
| Snowflake | Get started with Snowflake |
| Amazon Redshift | Get started with Amazon Redshift |
| Google BigQuery | Get started with Google BigQuery |
| Azure Synapse Analytics / Microsoft Fabric Warehouse | Get started with Azure Synapse Analytics and Microsoft Fabric Warehouse |
| Databricks | Get started with Databricks |
| Vertica | Get started with Vertica |
| Greenplum | Get started with Greenplum |
Patterns available for each warehouse
- ETL — Extract, transform, and load data in X: pull from any source, transform, load into the warehouse.
- Bulk load from cloud storage — Bulk load files in (S3 / GCS / Azure Storage / server) into X: high-throughput load using the warehouse's native COPY command.
- ELT — ELT with X: push raw data into the warehouse first, transform with SQL there.
- Reverse ETL — Reverse ETL with X: warehouse as the source, push curated data back to operational systems.
Each per-warehouse section in the Integration Use Cases category covers all of these patterns; pick the warehouse and start there.