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
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.