Redbird AI automates the data flow between your S3 data lake and dbt transformation layer. Stop manually tracking new files, writing custom ingestion scripts, or wrestling with source freshness checks across storage and transformation systems.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically kick off dbt model execution when source files arrive in designated S3 buckets. Redbird monitors S3 paths, validates file schemas, and triggers the appropriate dbt models to process fresh data without manual intervention or scheduled guesswork.
Keep dbt source YAMLs in sync with your S3 data lake structure automatically. When new buckets or file patterns are added to S3, Redbird generates the corresponding dbt source configurations, table definitions, and freshness checks so your project documentation never falls behind.
Automatically write dbt seed files and snapshot outputs to versioned S3 paths for long-term storage. Redbird orchestrates the export process, creates organized folder structures by run date, and maintains a queryable archive of dimension changes and reference data outside your warehouse.
Route dbt source freshness test failures to the teams that own upstream S3 data pipelines. Redbird parses dbt test results, identifies which S3 sources are stale, and sends context-rich alerts with bucket paths and expected vs actual file timestamps to data engineering channels.
Tag S3 objects with downstream dbt model information and transformation lineage automatically. When files are uploaded to source buckets, Redbird annotates them with metadata about which dbt models depend on them, helping data teams understand impact before modifying or archiving storage paths.
Create comprehensive data freshness dashboards by combining S3 last-modified timestamps with dbt source configurations. Redbird aggregates file metadata across buckets, compares against dbt freshness thresholds, and produces automated reports showing data lake health and ingestion lag across all sources.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Amazon S3 and dbt 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 both S3 bucket structures and file patterns alongside dbt project structure, source definitions, and model dependencies—so automations work with your existing data architecture.
Redbird's AI parses S3 bucket hierarchies, file naming conventions, and object metadata alongside your dbt project files—including sources.yml definitions, model SQL, and manifest.json lineage. It understands which S3 paths map to which dbt sources, how freshness checks are configured, and which models depend on specific storage locations. This means you can automate workflows based on actual schema changes, file arrivals, and transformation dependencies without hardcoding bucket names or model references.
faster to sync S3 sources with dbt configurations than manual YAML updates
Redbird can pull from Amazon S3 and dbt 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 Amazon S3 or dbt.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from Amazon S3 into dbt, or from dbt back into Amazon S3. 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 any S3 file event or dbt execution result, and take action across both your storage and transformation layers.
Fires when objects are created in specified buckets or prefixes, with filters for file type and naming patterns.
Triggers when object tags, storage class, or custom metadata fields change on existing files.
Detects when objects are deleted from watched paths, useful for tracking source data retention.
Write data, reports, or transformation outputs to specified S3 locations with custom naming and metadata.
Add or update tags on S3 files programmatically based on downstream processing or lineage information.
Reorganize storage, promote files between environments, or create backups across bucket hierarchies.
Fires after specific models finish executing, with success/failure status and row count metadata.
Triggers when source data in your warehouse falls outside configured freshness thresholds.
Detects data quality issues when schema tests, uniqueness checks, or custom tests return failures.
Execute targeted model runs, full DAG refreshes, or tag-based selections programmatically from any trigger.
Modify source configurations, freshness checks, or documentation programmatically as S3 structures change.
Compile and deploy updated dbt docs automatically after model changes or on a schedule.
Automate the workflows between Amazon S3 and dbt that your data team runs manually today. Redbird AI connects your data lake to your transformation layer with intelligence built in.