Cloud Storage · Source connector
S3 ELT Connector
Extract brings S3 cloud storage data into data warehouses and databases for analysis with the rest of your business data. Teams use it to organize file-based data for governed reporting and downstream processing. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
File-based data consolidation
Combine S3 file data with operational datasets for governed reporting and analysis.
Recurring data processing
Refresh S3 file-based inputs on a flexible schedule to keep downstream models current.
Data quality checks
Compare S3 file contents with expected schemas and record totals to identify incomplete deliveries.
Flexible reporting cadence
Refresh S3 reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make S3 cloud storage data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Connector data
Configured streams
Configure the API resources or reports to replicate when creating the Extract connection.
Schema
Data model at a glance
Connector pairings
Popular destinations
Commonly paired with S3 in Extract workflows.
Why Extract
Why Extract for S3
- Rust-native performanceUse Rust-native data movement to refresh S3 reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect S3 API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine S3 data with customer and finance models, then send approved outputs through a separately configured Reverse ETL destination.
FAQ
Frequently asked questions
How does Extract handle incremental and full S3 syncs?
An incremental sync processes only new or changed data since a stored checkpoint or new date partition, while a full sync processes the complete selected dataset or date range again. By avoiding repeated processing and writes for the full dataset, incremental syncs can reduce destination database updates and warehouse compute costs. Dynamic stream configuration supports incremental changes, partition-based extraction. Behavior depends on stream settings. Extract records run details so teams can verify each sync.
Can I backfill historical S3 data with Extract?
Historical coverage for S3: No connector-specific backfill parameter is exposed. Initial historical coverage depends on the stream and provider. Use the setup guide and run details to confirm the available source records and selected settings.
How can teams monitor S3 data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect S3 API activity, execution steps, and timing. This helps teams distinguish a source-data delay from a failed or incomplete run.
How often can Extract sync data from S3?
Extract supports schedules every 15 minutes, hourly, daily, or on a custom cadence. Teams can choose a frequency that matches routine reporting, time-sensitive monitoring, or operational activation needs.
What does the S3 data model look like in the destination?
The Available streams table describes the datasets Extract can read from S3. Use the connector documentation link for the available schema details and configuration requirements.