I'm exactly the target audience for this type of tool, a tech leader that has implemented a data warehouse and BI strategy. Some concrete tips for adoption:
- Break the dependency on your product. I need to be able to use the library even if your company goes under.
- Add a dbt library that makes it easy to use hashquery within dbt models. It gets you materialization for free and will answer a lot of questions you will get about dbt integration.
To comment more broadly, if you want to be a broad solution, the going trend in data integration seems to be at the warehouse level so you need to have SQL answers.
A bunch of tools all consume from our warehouse (existing BI, reverse ETL, data science systems). A BI definition tool won't work if I can't define segments in a way that all of those can access, even as just tables or views.
Our programmers and data science people know Python and are often very good at SQL, but their time is short and BI projects depending on them have been delayed. Our analysts know SQL, and have the dedicated time to make these projects happen.
This kind of code snippet isn't crazy to put into dbt, and if someone wants to do Python magic in the background they can:
hq_project.models.events_model.with_activity_schema(
group='user_id', timestamp='timestamp', event_key='event_type'
).funnel("ad_impression", "add_to_cart", "buy").as_sql()
- Break the dependency on your product. I need to be able to use the library even if your company goes under.
- Add a dbt library that makes it easy to use hashquery within dbt models. It gets you materialization for free and will answer a lot of questions you will get about dbt integration.
To comment more broadly, if you want to be a broad solution, the going trend in data integration seems to be at the warehouse level so you need to have SQL answers.
A bunch of tools all consume from our warehouse (existing BI, reverse ETL, data science systems). A BI definition tool won't work if I can't define segments in a way that all of those can access, even as just tables or views.
Our programmers and data science people know Python and are often very good at SQL, but their time is short and BI projects depending on them have been delayed. Our analysts know SQL, and have the dedicated time to make these projects happen.
This kind of code snippet isn't crazy to put into dbt, and if someone wants to do Python magic in the background they can: