This package models Pendo data from Fivetran's connector. It uses data in the format described by this ERD.
This package enables you to better understand how users are experiencing and adopting your product. It does so by:
- Calculating usage of features, pages, guides, and the overall product at the account and individual visitor level
- Enhancing event stream tables with visitor and product information, referring pages, and features to track the customer journey through the application
- Creating daily activity timelines for features, pages, and guides that reflect their adoption rates, discoverability, and usage promotion efficacy
- Directly ties visitors and features together to determine activation rates, power-usage, and churn risk
This package contains transformation models, designed to work simultaneously with our Pendo source package. A dependency on the source package is declared in this package's packages.yml file, so it will automatically download when you run dbt deps. The primary outputs of this package are described below. Intermediate models are used to create these output models.
| model | description |
|---|---|
| pendo__account | Each record represents a unique account in Pendo, enriched with metrics regarding associated visitors and their feature, page, and overall product activity (total and daily averages). Also includes their aggregated NPS ratings and the frequency and longevity of their product usage. |
| pendo__feature | Each record represents a unique tagged feature in Pendo, enriched with information about the page it lives on, the application and product area it is a part of, and the internal users who created and/or updated it last. Also includes metrics regarding the visitors' time spent using the feature and click-interactions with individual visitors and accounts. |
| pendo__page | Each record represents a unique tagged page in Pendo, enriched with information about its URL rules, the application and product area it is a part of, the internal users who created and/or updated it last, and the features that are currently live on it. Also includes metrics regarding the visitors' time spent on the page and pageview-interactions with individual visitors and accounts. |
| pendo__visitor | Each record represents a unique visitor in Pendo, enriched with metrics regarding associated accounts and the visitor's latest NPS rating, as well as the frequency, longevity, and average length of their daily product usage. |
| 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. |
| pendo__account_daily_metrics | A daily historical timeline of the overall product, feature, and page activity associated with each account, along with the number of associated users performing each kind of interaction. |
| pendo__feature_daily_metrics | A daily historical timeline, beginning at the creation of each feature, of the accounts and visitors clicking on each feature, the average daily time spent using the feature, and the percent share of total daily feature activity pertaining to this particular feature. |
| pendo__page_daily_metrics | A daily historical timeline, beginning at the creation of each page, of the accounts and visitors loading on each page, the average daily time spent on the page, and the percent share of total daily pageview activity pertaining to this particular page. |
| pendo__visitor_daily_metrics | A daily historical timeline of the overall product, feature, and page activity tracked for an individual visitor. Includes the daily number of different pages and features interacted with. |
| pendo__guide_daily_metrics | A daily historical timeline of the accounts and individual visitors interacting with guides via different types of actions. |
| pendo__feature_event | The event stream of clicks on tagged features in Pendo. Enriched with any visitor and/or account passthrough columns, the previous feature and page that the visitor interacted with, the application and platform the event occurred on, and information on the feature and its product area. |
| pendo__page_event | The event stream of views of tagged pages in Pendo. Enriched with any visitor and/or account passthrough columns, the previous page that the visitor interacted with, the application and platform the event occurred on, and information on the page and its product area. |
| pendo__guide_event | The event stream of different kinds of interactions visitors have with guides. Enriched with any visitor and/or account passthrough columns, as well as the application and platform that the event occurred on. |
| pendo__visitor_feature | Each record represents a unique combination of visitors and features, aimed at making "power-users" of particular features easy to find. Includes metrics reflecting the longevity and frequency of feature usage. |
Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages.
# packages.yml
packages:
- package: fivetran/pendo
version: [">=0.1.0", "<0.2.0"]By default, this package looks for your Pendo data in the pendo schema of your target database. If this is not where your Pendo data is, add the following configuration to your dbt_project.yml file:
# dbt_project.yml
...
config-version: 2
vars:
pendo_database: your_database_name
pendo_schema: your_schema_name This package includes all of the source columns that are defined in the macros folder. We recommend including custom columns in this package because the transformation models only bring in the standard columns for the EVENT, FEATURE_EVENT, PAGE_EVENT, ACCOUNT_HISTORY, and VISITOR_HISTORY tables.
You can add more columns using our passthrough column variables. These variables allow the passthrough columns to be aliased (alias) and casted (transform_sql) if you want, although it is not required. You can configure the datatype casting using a SQL snippet within the transform_sql key. You may add the desired SQL snippet while omitting the as field_name part of the casting statement - we rename this column with the alias attribute - and your custom passthrough columns will be casted accordingly.
Use the following format for declaring the respective passthrough variables:
# dbt_project.yml
vars:
pendo__feature_event_pass_through_columns: # will be passed to pendo__feature_event and stg_pendo__feature_event
- name: "custom_crazy_field_name"
alias: "normal_field_name"
pendo__page_event_pass_through_columns: # will be passed to pendo__page_event and stg_pendo__page_event
- name: "property_field_id"
alias: "new_name_for_this_field_id"
transform_sql: "cast(new_name_for_this_field as int64)"
- name: "this_other_field"
transform_sql: "cast(this_other_field as string)"
pendo__account_history_pass_through_columns: # will be passed to pendo__account, pendo__feature_event, and pendo__page_event
- name: "well_named_field_1"
pendo__visitor_history_pass_through_columns: # will be passed to pendo__visitor, pendo__feature_event, and pendo__page_event
- name: "well_named_field_2"
pendo__event_pass_through_columns: # will be passed to stg_pendo__event (in source package only)
- name: "well_named_field_3"By default, this package builds the Pendo final models within a schema titled (<target_schema> + _pendo), intermediate models in (<target_schema> + _int_pendo), and staging models within a schema titled (<target_schema> + _stg_pendo) in your target database. If this is not where you would like your modeled Pendo data to be written to, add the following configuration to your dbt_project.yml file:
# dbt_project.yml
...
models:
pendo:
+schema: my_new_schema_name # leave blank for just the target_schema
intermediate:
+schema: my_new_schema_name # leave blank for just the target_schema
pendo_source:
+schema: my_new_schema_name # leave blank for just the target_schemaNOTE: If your profile does not have permissions to create schemas in your destination, you can set each
+schemato blank. The package will then write all tables to your pre-existing target schema.
Don't see a model or specific metric you would have liked to be included? Notice any bugs when installing and running the package? If so, we highly encourage and welcome contributions to this package!
Please create issues or open PRs against main. Check out this post on the best workflow for contributing to a package.
This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.
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