Pendo dbt Package
This dbt package transforms data from Fivetran's Pendo connector into analytics-ready tables.
Resources
- Number of materialized models¹: 68
- Connector documentation
- dbt package documentation
What does this dbt package do?
This package enables you to understand how users are experiencing and adopting your product. It creates enriched models with metrics focused on feature usage, page activity, and guide interactions.
Output schema
Final output tables are generated in the following target schema:
<your_database>.<connector/schema_name>_pendo
Final output tables
By default, this package materializes the following final tables:
| Table | Description |
|---|---|
| pendo__account | Consolidates account profiles with visitor counts, aggregated NPS ratings (min, max, avg), activity metrics (events, minutes, active days/months), feature and page interaction counts, and daily averages to measure account engagement and product adoption. Example Analytics Questions:
|
| pendo__feature | Provides comprehensive feature profiles with page and product area associations, creator/updater details, core event tagging, and engagement metrics (clicks, visitors, accounts, time spent) to identify high-value features and optimize feature placement. Example Analytics Questions:
|
| pendo__page | Consolidates page profiles with URL rules, product area associations, active feature counts, creator/updater details, and pageview metrics (visitors, accounts, time spent) to analyze page performance and optimize page structure. Example Analytics Questions:
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| pendo__visitor | Provides individual visitor profiles with account associations, latest NPS rating, browser/OS details, activity metrics (events, minutes, active days/months), and daily averages to segment users and analyze engagement patterns. Example Analytics Questions:
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| pendo_guide | Each record represents a unique guide presented to visitors via Pendo. Includes metrics about the number of visitors and accounts performing various activities upon guides, such as completing or dismissing them. Example Analytics Questions:
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| pendo__account_daily_metrics | Chronicles daily account activity timelines with visitor counts (active, page viewing, feature clicking), event metrics (minutes, events, records), and distinct page/feature interaction counts to track account engagement trends and identify usage patterns. Example Analytics Questions:
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| pendo__feature_daily_metrics | Tracks daily feature engagement from creation with visitor/account counts (total, first-time, returning), click metrics, time spent, and relative share percentages to measure feature adoption velocity and compare feature popularity. Example Analytics Questions:
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| pendo__page_daily_metrics | Chronicles daily page views from creation with visitor/account counts (total, first-time, returning), pageview metrics, time spent, and relative share percentages to measure page adoption and compare page traffic patterns. Example Analytics Questions:
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| pendo__visitor_daily_metrics | Tracks daily visitor activity timelines with event metrics (minutes, events, records) and distinct page/feature interaction counts to monitor individual engagement patterns and identify power users. Example Analytics Questions:
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| pendo__guide_daily_metrics | Chronicles daily guide interactions with visitor/account counts (total, first-time), event counts, and action-specific metrics (guideSeen, guideDismissed, guideActivity, guideAdvanced, guideTimeout, guideSnoozed) to measure guide effectiveness and completion rates. Example Analytics Questions:
|
| pendo__feature_event | Streams individual feature click events with visitor/account IDs, timing, event/minute counts, previous feature context, IP/user agent details, and feature/page/product area associations to enable granular clickstream analysis and user journey mapping. Example Analytics Questions:
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| pendo__page_event | Streams individual page view events with visitor/account IDs, timing, event/minute counts, previous page context, IP/user agent details, and page/product area associations to enable page flow analysis and session path tracking. Example Analytics Questions:
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| pendo__guide_event | Streams individual guide interaction events with visitor/account IDs, event type (guideSeen, guideDismissed, guideActivity, guideAdvanced, guideTimeout, guideSnoozed), step details, timing, location data, and app/platform context to analyze guide completion funnels and identify friction points. Example Analytics Questions:
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| pendo__visitor_feature | Maps visitor-feature relationships with first/last click timing, total clicks, time spent, active days, and daily averages to identify feature power users, analyze feature stickiness, and segment users by feature engagement depth. Example Analytics Questions:
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¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.
Prerequisites
To use this dbt package, you must have the following:
- At least one Fivetran Pendo connection syncing data into your destination.
- A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
How do I use the dbt package?
You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:
- To add the package in the Fivetran dashboard, follow our Quickstart guide.
- To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.
How is this package maintained and can I contribute?
Package Maintenance
The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.
Contributions
A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.
We highly encourage and welcome contributions to this package. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.