Business Applications · Source connector
Asana ELT Connector
Extract brings Asana business applications data into data warehouses and databases for analysis with the rest of your business data. Teams use it to analyze operational activity, account changes, and business-process performance. Flexible scheduling keeps reporting current for routine analysis and time-sensitive decisions.
View setup guide
Use cases
What teams use this connector for
Operational reporting
Analyze Asana activity alongside account, finance, and process data to monitor business operations.
Record reconciliation
Compare Asana records with internal systems to identify missing, stale, or inconsistent business data.
Workflow prioritization
Use Asana data to prioritize accounts, tasks, and operational follow-up based on modeled business rules.
Flexible reporting cadence
Refresh Asana reporting every 15 minutes, hourly, daily, or on a custom schedule as business needs change.
Warehouse and database analysis
Use Extract to make Asana business applications data available in data warehouses and databases such as Snowflake, BigQuery, Redshift, and PostgreSQL for governed analysis.
Connector data
Available streams
| Stream | Description | Sync mode |
|---|---|---|
custom_fields | Fields include gid, resource_type, name, type. | Full refresh |
goal_parent_goals | Fields include gid, resource_type, name, html_notes. | Full refresh |
goals | Fields include gid, resource_type, name, html_notes. | Full refresh |
portfolio_items | Fields include gid, resource_type, name, resource_subtype. | Full refresh |
portfolios | Fields include gid, resource_type, name, resource_subtype. | Full refresh |
projects | Fields include gid, resource_type, name, resource_subtype. | Incremental |
tags | Fields include gid, resource_type, name, color. | Full refresh |
tasks | Fields include gid, resource_type, name, resource_subtype. | Incremental |
team_memberships | Fields include gid, resource_type, user, team. | Full refresh |
teams | Fields include gid, resource_type, name, description. | Full refresh |
users | Fields include gid, resource_type, name, email. | Full refresh |
workspaces | Fields include gid, resource_type, name, email_domains. | Full refresh |
Schema
Data model at a glance
custom_fields
gidstringresource_typestringnamestringtypestringenum_optionsjsonenabledbooleanrepresentation_typestringid_prefixstring
goal_parent_goals
gidstringresource_typestringnamestringhtml_notesstringnotesstringdue_onstringstart_onstringis_workspace_levelboolean
goals
gidstringresource_typestringnamestringhtml_notesstringnotesstringdue_onstringstart_onstringis_workspace_levelboolean
portfolio_items
gidstringresource_typestringnamestringresource_subtypestringportfolio_gidstring
portfolios
gidstringresource_typestringnamestringresource_subtypestringarchivedbooleancolorstringstart_onstringdue_onstring
projects
gidstringresource_typestringnamestringresource_subtypestringarchivedbooleancolorstringiconstringcreated_atstring
No verified relationships are available in this schema preview.
Connector pairings
Popular destinations
Commonly paired with Asana in Extract workflows.
Why Extract
Why Extract for Asana
- Rust-native performanceUse Rust-native data movement to refresh Asana reporting every 15 minutes, hourly, daily, or on a custom schedule as volume grows.
- Full run transparencyInspect Asana API activity, execution steps, and timing in Extract's run-level logs when data needs review.
- ELT + Reverse ETL in oneCombine Asana 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 Asana 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. Projects and tasks use incremental changes; other streams use full refresh. Extract records run details so teams can verify each sync.
Can I backfill historical Asana data with Extract?
Historical coverage for Asana: The initial run lists currently accessible resources without a cursor cutoff. Later incremental runs filter projects and tasks by the cursor. This is not an audit history of deleted records or prior versions; no date-window backfill setting is exposed. Use the setup guide and run details to confirm the available source records and selected settings.
How can teams monitor Asana data quality and freshness?
Compare expected reporting periods and record totals in your models, then use Extract's run-level logs to inspect Asana 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 Asana?
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 Asana data model look like in the destination?
The Available streams table describes the datasets Extract can read from Asana. The inline schema previews entities and fields. Only relationships verified from the exported schema are shown. Use the full schema link for more details.