The pirate Jack Sparrow wants to know what Ad campaign got him most customers [almost all startups face this problem].
Following is the Ad analytics capturing infrastructure of Jack Sparrow’s Startup.
Signup / Conversion data of Jack Sparrow has the following data flow
Now the data warehouse has ads data and sign up data under one roof. Next step is to connect a BI tool like Mode Analytics, Chartio or looker and query the data using SQL. Although this method works and gets us desired results this has a lot of issues.
Data in our data warehouse is not validated against Foreign key primary key relationship The entire schema and all columns have to be exposed to the BI tool and to the end user to successfully build dashboards No failure logging capability around data migration from 3rd party data and production DB to data warehouse
In order to circumvent these issues we bring data build tool into play.
Using DBT we can perform
Foreign key primary key relationship Use Jinja to slice and dice data Use snapshot to go back in time
Plugging DBT into our data warehouse looks like the following
Now we just have to connect the Analytics schema to the BI tool and our end users need to query consolidated tables to build dashboards
Advantages of this style of data engineering are
We receive alerts when the system encounters errors Data quality is maintained Only very few tables are exposed to internal end users Internal end users need not write complex joins to get the desired output
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