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Tools and workflows

We’re working on automation around how we manage dbt (or similar) schemas and tests for ourselves – particularly maintaining dbt models (schemas and tests) for the ever-changing raw analytics events, updated with every feature release. Our trigger was that we just finished an internal hackathon where we ended up building a dbt utility that we’re excited to take further and open source.

Right now we’re particularly focusing on metrics that we compute by tying together those ever-changing raw analytics events with data from our backend and 3rd party tools like Stripe.

This survey is a mix of situational questions (your data stack) and open questions on how you manage the steps from when a success metric has been defined (pre feature release) until the insights are visualized.

Below is a list of steps we typically see in analytics pipelines for new feature releases:
1. Plan: how success is defined, how metrics look like and what events and properties are needed to structure metrics
2. Track: Send analytics events from product
3. Pipe: Pipe analytics events to where they are stored
4. Store: The database where analytics events are stored
5. Transform: Calculate derived tables from raw analytics tables, based on metrics
6. Visualize / trigger campaigns: Visualize derived tables in a visualization tool or trigger marketing campaigns based on them

Note: this is for research purposes only and we will never use your responses to try to sell you anything. 

We will randomly select 5 participants to receive a $100 gift certificate. Those that complete the survey before midnight on Tuesday, August 3rd (PST) will be eligible.

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* 1. Please share your email to be eligible for receiving a $100 gift certificate from Amazon

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* 2. Where do you plan your metrics to measure the success of your feature releases (step 1)?

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* 3. Where do you maintain a tracking plan for the structures of your event based behavioural data? (step 1)

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* 4. What tool(s) do you use to send event based behavioural data from your application (step 2)

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* 5. What tool do you use to you pipe event based behavioural data into a data warehouse? (step 3)

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* 6. What data warehouse do you use to store and analyze raw event based behavioural data? (step 4)

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* 7. If you manage schemas for downstream computations based on event data, what tool do you use to do that (step 5)?

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* 8. If you manage automatic computation of downstream computations, what tool do you use to do that (automation of step 5)?

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* 9. What tool do you use to visualize data stored in your data warehouse? (step 6)

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* 10. Please describe the main challenges and friction points in the process around the tools you selected above

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