Automatically sync your dbt transformation layer with PostHog product analytics. Stop manually updating event taxonomies when models change, chasing down metric definitions, or reconciling analytics tracking with your warehouse schema.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically append dbt model names, tags, and lineage information to PostHog events based on underlying table mappings. Analytics teams see which transformation layer feeds each product metric, enabling faster debugging and impact analysis when models change.
When dbt metric definitions change, update corresponding PostHog insights and dashboard descriptions with the canonical business logic. Ensures product teams always reference the same calculations as your data warehouse, eliminating definition drift between systems.
Monitor PostHog event properties and send Slack alerts when product analytics reference tables or columns marked for deprecation in dbt. Prevents breaking changes by giving analytics engineers visibility into downstream product tracking dependencies before refactoring.
Automatically create dbt data quality tests based on PostHog event property definitions and expected value ranges. When product teams define strict event schemas in PostHog, corresponding warehouse validation tests ensure upstream data pipelines stay compliant.
Sync dbt column descriptions, data types, and semantic tags to PostHog's event property definitions. Product managers browsing PostHog insights see the same field documentation analytics engineers maintain in dbt, creating a single source of truth across tools.
When dbt data quality tests fail on models feeding PostHog event tables, automatically log issues in PostHog with context about affected dashboards and insights. Product teams get proactive alerts when analytics accuracy is compromised, not days later when numbers look wrong.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize dbt and PostHog with OAuth or API credentials. Redbird never stores your data — it just passes through.
Tell Redbird what to do in plain language — no SQL, no code, no configuration files required.
Redbird shows you exactly what it will do before running anything. Approve the workflow, set a schedule, and switch it on.
Workflows run on your schedule or on triggers. Every run is logged. Adjust with natural language at any time.
Redbird understands dbt's transformation DAG structure and PostHog's event schema architecture, automatically mapping warehouse models to product analytics tracking without custom code.
Redbird parses dbt manifest.json and catalog files to understand your entire transformation layer, then matches table names and column schemas to PostHog event properties and person attributes. The AI recognizes semantic patterns like fact tables feeding event streams, metric calculations spanning both systems, and naming conventions linking warehouse columns to analytics properties. When dbt models reference staging tables that source PostHog raw data, Redbird automatically identifies circular dependencies and bidirectional sync opportunities.
faster than building dbt macros and PostHog API scripts to sync metadata
Redbird can pull from dbt and PostHog simultaneously, merge the results, and format a polished report — sent on a schedule or on demand.
Set conditions in natural language. Get notified in Slack or email the moment a threshold is crossed in either dbt or PostHog.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from dbt into PostHog, or from PostHog back into dbt. Resolve conflicts with configurable merge rules.
Every workflow run is logged — what ran, what changed, and why. Replay or revert any individual step at any time.
Start from any dbt model change or PostHog event pattern, then automate actions across both platforms instantly.
Fires when a specific dbt model or tag group finishes executing successfully in your warehouse.
Triggers when data quality tests fail on models, with severity level and affected column context.
Detects updates to model descriptions, column definitions, tags, or metric calculations in your dbt project.
Write or append descriptions, tags, and metadata to dbt schema YAML files programmatically.
Create new data quality tests based on external schema definitions or validation rules.
Apply semantic tags to models for downstream tool categorization or orchestration logic.
Fires when event property definitions, expected types, or validation rules change in PostHog.
Triggers when dashboards, funnels, or retention analyses are built using specific event types.
Detects when product teams create new event actions or conversion goals requiring warehouse support.
Sync canonical definitions and business context from your data warehouse to PostHog's data dictionary.
Add timeline annotations to charts when upstream data quality issues or schema changes occur.
Apply metadata tags to insights and dashboards based on underlying dbt model categorization.
Sync dbt transformations with PostHog analytics in minutes. Keep your warehouse models and product tracking aligned automatically, no engineering overhead required.