> For the complete documentation index, see [llms.txt](https://docs.coupler.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.coupler.io/sources/category/sales/clazar/best-practices.md).

# Best Practices

## Recommended setup

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><strong>Group entities by purpose</strong></td><td>Create separate data flows for operational data (buyers, contracts, opportunities) and analytics data (revenue, disbursements, insights). This keeps runs fast and makes troubleshooting easier.</td></tr><tr><td><strong>Use Append for cross-cloud revenue</strong></td><td>AWS, Azure, and GCP revenue entities share common fields like period and amount. Use the Append transformation to combine them into one unified revenue table instead of managing three separate sheets.</td></tr><tr><td><strong>Join opportunities with contracts</strong></td><td>Connect the Opportunities and Contracts entities using a Join transformation to calculate conversion rates from deal stage to signed contract — a key metric for marketplace sales teams.</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>Match refresh cadence to entity type</strong></td><td>Operational entities like buyers and opportunities can refresh daily. GCP monthly insights only update once a month — refreshing them daily wastes runs without gaining new data.</td></tr><tr><td><strong>Use incremental GCP entities for daily tracking</strong></td><td>For day-over-day GCP monitoring, use the incremental daily insights entity rather than the full daily insights. It returns only new changes, keeping your destination lean.</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>Don't pull all analytics entities at once</strong></td><td>Clazar has 15+ analytics entities. Only add the ones relevant to your active marketplaces. If you don't sell on GCP, skip all GCP entities entirely.</td></tr><tr><td><strong>Send financial data to BigQuery</strong></td><td>Revenue, disbursement, and usage entities can grow large over time. BigQuery handles volume better than Google Sheets and makes cross-entity SQL joins much easier.</td></tr></tbody></table>

## Common pitfalls

{% hint style="danger" %}
Don't mix incremental and non-incremental GCP entities in the same destination table. Incremental entities return delta records; non-incremental entities return full snapshots. Appending both will produce duplicate rows.
{% endhint %}

{% columns %}
{% column %}

### Do

* Use non-incremental entities for initial historical backfills
* Reconcile AWS revenue against disbursements with a time-shifted comparison (30–60 day lag)
* Test with a manual run before scheduling any analytics entity
  {% endcolumn %}

{% column %}

### Don't

* Assume co-sell opportunities cover your full pipeline — they only reflect partner-referred deals
* Use the same API key across too many concurrent data flows — stagger runs to stay within rate limits
* Rely on Azure metered usage data if your listing uses flat-rate pricing
  {% endcolumn %}
  {% endcolumns %}


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