.. _2025-02-17-activity-schema-enrichment-rules_release:

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2025-02-17 - Activity Schema Enrichment Rules
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Release
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Get More Insights with Enhanced Activity Data!

We're excited to announce a new feature that makes it even easier to analyze your activity data and gain valuable insights. 

**The Problem:** While our new activity schema tiles and rules are great at collecting data, they weren't always easy to use for reporting. 

**The Solution:** We've introduced a new activity consume template that automatically adds a set of pre-calculated columns to your consume tile. These columns provide you with ready-to-use information, saving you time and effort.

**Here's what you get:**

* **concept_first_seen:**  The first time a concept was seen in your data.
* **activity_first_seen:** The first time an activity was seen.
* **activity_last_seen:** The last time an activity was seen.
* **activity_occurrence:** The number of times an activity occurred.
* **activity_repeated_at:**  The time intervals between activity occurrences.
* **value_increased:**  Indicates if the value increased during the activity.
* **value_decreased:** Indicates if the value decreased during the activity.
* **value_change:** The amount of change in value during the activity.
* **opening_balance:** The value at the start of the activity.
* **closing_balance:** The value at the end of the activity.

**The Benefits:**

* **Faster Insights:**  Get answers to your questions quickly with pre-calculated data.
* **Simplified Reporting:**  No need to write complex SQL queries.
* **Enhanced Analysis:**  Explore your data with a richer set of attributes.

**How it Works:**

We use our powerful Jinja templating system to automatically generate these columns, making complex SQL features accessible with a simple click.

**Start exploring your activity data with these new insights today!**

Magician Partner
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if(row_number() over (partition by driving_concept order by activity_timestamp asc)=1,true,false) as concept_first_seen,',
      'if(row_number() over (partition by driving_concept,activity order by activity_timestamp asc)=1,true,false) as activity_first_seen,',
      'if(row_number() over (partition by driving_concept,activity order by activity_timestamp desc)=1,true,false) as activity_last_seen,',
      'row_number() over (partition by driving_concept,activity order by activity_timestamp asc) as activity_occurrence,',
      'lead(activity_timestamp) over (partition by driving_concept,activity order by activity_timestamp asc) as activity_repeated_at,',
      'if(coalesce(lag(revenue_impact) OVER (PARTITION BY driving_concept,activity ORDER BY activity_timestamp ASC),0) < revenue_impact,TRUE,FALSE) as value_increased,',
      'if(lag(revenue_impact) OVER (PARTITION BY driving_concept,activity ORDER BY activity_timestamp ASC) > revenue_impact,TRUE,FALSE) as value_decreased,',
      'revenue_impact - coalesce(lag(revenue_impact) OVER (PARTITION BY driving_concept,activity ORDER BY activity_timestamp ASC),0) as value_change,',
      'coalesce(sum(coalesce(revenue_impact, 0)) over (partition by driving_concept,activity order by activity_timestamp rows between unbounded preceding and 1 preceding), 0) as opening_balance,',
      'sum(coalesce(revenue_impact, 0)) over (partition by driving_concept,activity order by activity_timestamp) as closing_balance,'

Last Refreshed
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*Doc Refreshed: 2025-02-16*