Cloud Storage · Destination connector
Azure Data Lake Storage Gen2 ELT Connector
Extract delivers approved, modeled business data to Azure Data Lake Storage Gen2. Teams use it to export governed datasets for file-based partner and operational workflows and keep those downstream actions current on a flexible schedule.
View setup guide
Use cases
What teams use this connector for
Modeled file exports
Use Extract to export approved business datasets to Azure Data Lake Storage Gen2 for file-based workflows.
Partner data delivery
Prepare governed records before making them available in Azure Data Lake Storage Gen2 for partners and downstream systems.
Recurring operational extracts
Keep current modeled files in Azure Data Lake Storage Gen2 for recurring reporting and operational processing.
Controlled data distribution
Apply validation and approval rules before distributing business data through Azure Data Lake Storage Gen2.
Governed scheduled delivery
Keep Azure Data Lake Storage Gen2 supplied every 15 minutes, hourly, daily, or on a custom schedule using data models that teams can test and approve.
Connector data
Data accepted by this destination
Receives the streams and fields selected in the source connection. Writes CSV, JSON Lines, or Parquet files, or Delta tables. There is no fixed destination stream catalog or entity relationship diagram.
Why Extract
Why Extract for Azure Data Lake Storage Gen2
- Rust-native performanceDeliver approved records to Azure Data Lake Storage Gen2 with Rust-native data movement on a schedule that matches the operational workflow.
- Full run transparencyInspect Azure Data Lake Storage Gen2 delivery execution, timing, and failures in Extract's run-level logs.
- ELT + Reverse ETL in oneBuild governed models from inbound data, then use Extract Reverse ETL to activate the approved records in Azure Data Lake Storage Gen2.
FAQ
Frequently asked questions
How does Extract handle incremental and full Azure Data Lake Storage Gen2 deliveries?
An incremental delivery processes only new or changed data since a stored checkpoint or new date partition, while a full delivery processes the complete selected dataset or date range again. By avoiding repeated processing and writes for the full dataset, incremental deliverys can reduce destination database updates and warehouse compute costs. Append CSV, JSON Lines, or Parquet files. Delta tables support Append and Replace, additive schema evolution, and same-stream-run retry protection. Extract records run details so teams can verify each delivery.
Can I backfill historical Azure Data Lake Storage Gen2 data with Extract?
Historical coverage for Azure Data Lake Storage Gen2: 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 should teams prepare data for Azure Data Lake Storage Gen2?
Prepare and validate the modeled records before delivery, including any eligibility, consent, or suppression rules required by the workflow. Then use the Azure Data Lake Storage Gen2 setup guide and Extract's run-level logs to validate each delivery.
How often can Extract deliver data to Azure Data Lake Storage Gen2?
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 Azure Data Lake Storage Gen2 data model look like in the destination?
Azure Data Lake Storage Gen2 receives the approved modeled records selected in the connection. The destination shape follows that validated model; use the setup guide to confirm any connector-specific field requirements.