Redbird AI automates the workflow between your transformation layer and data warehouse. Stop manually monitoring model runs, investigating failed tests, or copying lineage documentation between systems—let AI handle deployment validation, query optimization, and data quality reporting end-to-end.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When dbt models are deployed, automatically sync column descriptions, test definitions, and upstream dependencies to Snowflake table and column metadata. Analytics engineers get self-documenting warehouse schemas without manual annotation.
Monitor Snowflake query history alongside dbt test results to identify which model failures impact downstream BI queries. Automatically surface which dashboards or users are affected by failing transformations.
Analyze dbt run duration and resource consumption in Snowflake query logs. Automatically recommend warehouse scaling strategies, clustering keys, or materialization changes to reduce compute costs for specific model sets.
Compile dbt schema test outcomes, freshness checks, and Snowflake table growth statistics into unified quality dashboards. Surface anomalies like unexpected row count changes or test pass rate degradation across the warehouse.
When upstream source tables in Snowflake gain new columns or change data types, automatically update corresponding dbt source YAML files. Keep transformation code in sync with evolving warehouse schemas without manual audits.
Continuously capture execution times, bytes scanned, and partition pruning effectiveness for every dbt-generated query in Snowflake. Build long-term performance baselines to detect model regressions and justify warehouse optimizations.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize dbt and Snowflake 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 AI understands both dbt's DAG structure and Snowflake's warehouse architecture—mapping transformation logic to query execution and maintaining consistency across your entire analytics stack.
Redbird parses dbt manifest.json, run_results.json, and catalog files to understand model dependencies, test configurations, and materialization strategies. It simultaneously monitors Snowflake's ACCOUNT_USAGE views and INFORMATION_SCHEMA to track table lineage, query performance, and warehouse compute patterns. The AI automatically correlates dbt model names with their materialized Snowflake objects, matching transformation logic to actual warehouse execution behavior without manual mapping.
faster dbt-to-Snowflake deployment validation vs manual checks
Redbird can pull from dbt and Snowflake 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 Snowflake.
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 Snowflake, or from Snowflake 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 workflows from dbt model deployments, test outcomes, or Snowflake warehouse events—then take action in either system automatically.
Fires when any dbt model finishes execution, whether successful or failed.
Triggered when schema tests, data tests, or freshness checks fail in a dbt run.
Activates when source data staleness exceeds configured warn_after thresholds.
Modify schema.yml files or add column descriptions to dbt models programmatically.
Execute targeted dbt runs using selectors or model names to refresh specific transformations.
Create or update source.yml files based on external schema changes or new data sources.
Fires when columns are added, removed, or modified in monitored Snowflake tables.
Triggered when a warehouse exceeds defined compute credit consumption in a time period.
Activates when query duration deviates significantly from historical performance baselines.
Write metadata, documentation, or lineage information to table and column COMMENT fields.
Scale warehouse compute up or down based on workload patterns or scheduled events.
Generate zero-copy clones of production tables for testing or development environments.
Join analytics teams using Redbird AI to automate dbt-to-Snowflake workflows—from deployment validation to performance monitoring. Connect your transformation layer and data warehouse in minutes.