# Best Practices

## Recommended setup

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Use the "Changed after" filter for large workspaces</strong></td><td>If your workspace has thousands of tasks, set the Changed after date to limit exports to recently modified records. This avoids timeouts and keeps your destination lean.</td></tr><tr><td><strong>Export Tasks and Users as separate sources in one data flow</strong></td><td>Add both Tasks and Users as sources, then use the Join transformation on assignee ID. This gives you a clean table with task details and assignee info combined — ideal for workload dashboards.</td></tr><tr><td><strong>Use Append to combine multiple workspaces</strong></td><td>If your team operates across multiple Asana workspaces or accounts, add each as a separate source and use the Append transformation to merge them into a single dataset.</td></tr></tbody></table>

## Data refresh and scheduling

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Run incrementally, not as a full refresh</strong></td><td>For Tasks, use the Changed after filter combined with an Append destination mode. This adds only new or updated tasks each run rather than reloading everything — faster and less prone to timeouts.</td></tr><tr><td><strong>Schedule Stories exports less frequently</strong></td><td>Stories (comments and activity) grow quickly. A daily or weekly schedule is usually sufficient — hourly exports of Stories can be heavy on API calls and slow to process.</td></tr></tbody></table>

## Performance optimization

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Extract custom fields with json_extract</strong></td><td>Custom fields come back as a JSON blob. Use Coupler.io's calculated column with json_extract to surface the specific fields you need — this makes downstream filtering and reporting much easier.</td></tr><tr><td><strong>Send data to BigQuery for large-scale analysis</strong></td><td>If you're tracking hundreds of projects or need historical trend data, BigQuery handles Asana's volume much better than a spreadsheet. Use incremental loads to keep costs low.</td></tr></tbody></table>

## Common pitfalls

{% hint style="danger" %}
Do not connect a personal Asana project space. Asana's API blocks access to personal spaces entirely — you'll get a "Data cannot be fetched" error every time. Make sure you're using an organization workspace before setting up your data flow.
{% endhint %}

{% columns %}
{% column %}
**Do**

* Verify your account is part of an organization workspace before connecting
* Use the Changed after filter to scope large Task exports
* Use json\_extract for custom fields you need as separate columns
* Test with a manual run before setting up a schedule
  {% endcolumn %}

{% column %}
**Don't**

* Expect to filter Tasks by project ID at the source level — do this post-import
* Try to export media attachments — only structured data is supported
* Export all Stories on a high-frequency schedule — it's slow and rarely necessary
* Assume empty columns are a bug — fields like completed\_by.name are only populated in specific conditions
  {% endcolumn %}
  {% endcolumns %}