Redbird AI syncs dbt models, test results, and lineage metadata directly with your Redshift warehouse. Stop manually tracking failed tests, promoting models between environments, or piecing together transformation dependencies across your analytics stack.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When dbt tests fail during scheduled runs, automatically write failure details, affected rows, and test SQL to a monitoring table in Redshift. Track test history over time and trigger downstream alerts when critical models break quality thresholds.
After each dbt run, extract model descriptions, column definitions, tags, and owners from your project and write them to a governance schema in Redshift. Keep your data catalog, lineage, and documentation queryable alongside production data without maintaining separate systems.
Monitor query execution times and resource consumption for staging tables in Redshift. When new dbt models consistently outperform existing production versions, automatically update your dbt project configuration and trigger promotion workflows.
Compare live Redshift table structures against dbt model definitions on a schedule. When columns are added outside of dbt, data types change, or constraints differ, flag the drift and notify analytics engineers to reconcile definitions.
After every dbt job completes, query Redshift system tables for row counts, disk usage, sort key efficiency, and distribution stats for affected models. Append this operational context to dbt run results for performance tracking and optimization decisions.
Aggregate dbt test results stored in Redshift over rolling windows to produce model reliability scores, failure trend analysis, and data quality KPIs. Automatically distribute reports showing which models need attention and which teams own degrading pipelines.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize dbt and Redshift 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 project structure, manifest schemas, and test result formats alongside Redshift table definitions, system catalogs, and query execution metadata.
Redbird parses dbt manifest.json, run_results.json, and catalog.json files to understand your transformation graph, then cross-references this with Redshift's pg_catalog, SVV_TABLE_INFO, and STL_QUERY logs. It knows how dbt models map to Redshift tables, which tests validate which columns, and how lineage flows through both systems. When you ask Redbird to sync metadata or detect drift, it automatically matches model definitions to warehouse reality without custom mapping logic.
faster than writing custom Python scripts to sync dbt metadata with Redshift
Redbird can pull from dbt and Redshift 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 Redshift.
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 Redshift, or from Redshift 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 automations from dbt job events or Redshift table changes and take action in either system without writing code.
Fires when a dbt job finishes, whether successful or failed, with full run results and timing.
Triggers when any dbt test returns failures, including severity level and affected rows.
Activates when source freshness falls outside acceptable thresholds defined in your project.
Modify a model's YAML documentation or add column-level descriptions programmatically.
Apply or remove tags to group models by domain, sensitivity, or operational tier.
Initiate a specific job run in dbt Cloud with custom parameters or environment overrides.
Detects when a Redshift table's row count shifts beyond expected variance thresholds.
Fires when columns are added, removed, or altered on tracked tables outside of orchestrated workflows.
Triggers when specific queries or table scans exceed historical performance baselines.
Execute DDL to build new tables or swap production tables with validated staging versions.
Optimize table storage and update statistics to maintain query performance across workloads.
Write operational events, test results, or lineage records to centralized governance tables.
Sync dbt and Redshift in minutes. Automate test monitoring, metadata syncing, and schema validation without maintaining custom scripts or orchestration overhead.