Pipeline Overview

Understand what we’re running on your warehouse

This page is intended to give you a high level overview of the pipeline Statsig Warehouse Native will run on your warehouse.

Main Steps

The main steps in the pipeline are:

• Identifying users’ first exposures
• Annotating Metric Sources with exposure data
• Creating metric-user-day level staging data
• Running intermediate rollups for better performance
• Calculating group-level summary Statistics

Types of DAGs

Statsig lets you run your pipeline in a few different ways:

• A Full Refresh totally restates the experiment’s data and calculates it from scratch. This is useful for starting an experiment, or if underlying data has changed
• An Incremental Refresh appends new data to your experiment data. This reduces the cost of running scheduled updates to your results
• A Metric refresh allows you to update a specific metric in case you changed a definition, or want to add new metrics to your analysis

Artifacts and Entity Relationships

The following tables will be generated and stored in your warehouse per-experiment. You have full access to these data sources for your own analysis, models, or visualizations. For experiments, experiment_id will be the name of the experiment; for Feature Gates, experiment_id will be the name of the gate along with the specific rule ID (e.g. chatbot_llm_model_switch_31e9jwlgO1bSSznKntb2gp_exposures_summary)

This is not an exhaustive list, but includes most of the core result/staging tables that you might be interested in using for your own analysis. Note - These are internal tables and will change as the product evolves. Changes will be documented here.

Table	Description	Notes
first_exposures_<experiment_id>	Deduplicated and stitched (for experiments with ID resolution) first exposure events	Useful for ad-hoc analysis
exposures_summary_<experiment_id>	Timeseries of exposures per group for display in Pulse
unit_day_metrics_<experiment_id>	User-day level metric aggregations table	Useful for ad-hoc analysis
unit_covariate_metrics_<experiment_id>	User-level pre-experiment aggregations for regression adjustment/CUPED
funnel_events_<experiment_id>	Staging table for running funnel analysis
percentile_values_<experiment_id>	Staging table for running percentile analysis
distinct_values_<experiment_id>	Staging table for running count distinct analysis
windowed_metrics_<experiment_id>	Staging table for generating running totals when restating Pulse
ratio_aggregations_<experiment_id>	Staging table for generating running totals when restating Pulse
results_<rollup>_<experiment_id>	Outputs of Statistical Analysis for different rollups (e.g. daily, days-since-exposure, cumulative, 7-day). Exported to Statsig	Pulse inputs - useful for replicating Statistical analysis
ratio_results_<rollup>_<experiment_id>	Outputs of Statistical Analysis for ratio metrics in different rollups (e.g. daily, days-since-exposure, cumulative, 7-day). Exported to Statsig	Pulse inputs - useful for replicating Statistical analysis

The high level relationships/contents of these tables are represented below - refer to the Main Steps image below for scheduling details.

Other Jobs

Alongside and inside this main flow, Statsig will also:

• Run Health Checks and a Summary View for exposures
• Calculate top dimensions for dimensional metrics
• Calculate funnel steps
• Run CUPED and Winsorization procedures during the group-level summaries to reduce variance and outlier influence
• Calculate inputs to the Delta Method to avoid bias on Ratio and Mean metrics

Statsig generates experiment-level tables - this makes it easy to do your own follow-up analyses on specific experiments.

Visibility

Clicking into the history icon on your pulse results, you’ll be able to see the Jobs and IDs we ran for each pulse reload, alongside relevant information on compute time and cost.

This will also be fully transparent from your own Warehouse’s history and usage management, but having the costs in console is helpful knowledge for the cross-functional experimentation teams running the analysis.

Exposure Export Table

Statsig dedupes and records each user’s first exposure to an experiment into a table in your warehouse. This table name is configurable in the Data Connection setup and defaults to statsig_forwarded_exposures. This table contains each user’s first exposure to an experiment. For feature gates, we dedupe and record exposures for partial rollouts (e.g. 5% or 50% rollouts - but not 0% or 100% rollouts).

Column Name	Data Type	Description
experiment_id	string	The identifier for the gate/experiment
group_id	string	groupID for experiments; ruleID+Pass/Fail for gates
group_name	string	Name of the experiment group (e.g. Control vs Test)
user_id	string	The ID passed in as the Statsig userID
stable_id	string	Statsig Client SDK managed stable device identifier
[your custom ids]	string	One column for every custom unitID you use on Statsig
timestamp	timestamp	Statsig Client SDK managed stable device identifier
user_dimensions	object	Warehouse specific object with all the user dimensions
ts	timestamp	Timestamp of the first exposure