'We Built a Modern Data Stack for Startups' data datavisualization
And whatever else is needed to turn the source data into the 'ideal' data for querying.which cleans the source lead data and makes custom fields easy to consumewhich is built entirely from the nested opportunity data in the leads source, presented as you would want to query it
With that caveat that I'm only an amateur dbt'ist, it's worth noting some decisions that went into this structure:in dbt literature, described as the first transformation after source and appearing before staging. We've opted not to create these, and to go straight for staging tables - this avoids maintaining another layer of dbt schemas, and lots of our data is close to staging format at source anyway.
In terms of developing against dbt, each engineer has a separate BigQuery dataset that they target in local development for testing. Running a full build is quick right now, making local development enjoyable. Whether a tool is intuitive is subjective, but Metabase certainly feels that way to me. As an example, let's try and chart the number of incident timeline items created per month and see what that process looks like:Joining the Stg Product Timeline Items table with Stg Product Organisations was really easy, in part because Metabase can automatically determine join keys .
One example is visible in the previous screen recording, where joining Stg Product Timeline Items with Stg Product Organisations prefilled the join key . Metabase can only default this if it knows Organisation ID on Stg Product Timeline Items is a foreign key of Stg Product Organisations, and which field it references.
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