Redbird AI automates the flow between your BI layer and cloud data warehouse. Stop manually validating LookML models against Redshift schemas, debugging query performance across systems, or reconciling metric definitions with underlying table structures.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When tables or columns change in Redshift, instantly validate all dependent LookML explores and dimensions. Redbird identifies breaking changes before they impact dashboards and alerts analytics teams to update model definitions proactively.
Track slow-running Looker queries and analyze their execution patterns in Redshift. Automatically identify tables needing sort keys, distribution keys, or materialized views based on actual BI query patterns, not guesswork.
Capture row counts, null rates, freshness checks, and constraint violations from Redshift and surface them in Looker dashboards. Give data teams a single BI view of warehouse health without writing custom SQL.
Automatically log query patterns, dashboard usage, and user activity from Looker into Redshift tables. Build historical analytics on BI adoption, identify unused content, and track metric consumption across business units.
Continuously compare metric calculations in LookML against ground truth queries in Redshift. When definitions diverge or data quality issues cause discrepancies, notify data teams before executives see wrong numbers in dashboards.
Correlate Redshift query costs with the Looker users, dashboards, and explores that generated them. Identify which business teams or reports drive warehouse spend and optimize expensive queries with full context.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Looker 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 AI understands both LookML semantic models and Redshift database schemas, so you can automate validation, monitoring, and optimization across your BI and warehouse layers.
Redbird parses LookML explores, joins, and dimension definitions alongside Redshift table schemas, distribution keys, and query execution plans. It maps semantic layer metrics to underlying warehouse tables, validates field dependencies across both systems, and identifies performance bottlenecks from BI query patterns hitting specific Redshift structures. The AI understands when a Looker dimension references a derived table, how that table's materialization impacts query performance, and which distribution strategies would optimize the most-used dashboard queries.
faster to validate BI models against warehouse schemas than manual SQL testing
Redbird can pull from Looker 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 Looker 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 Looker into Redshift, or from Redshift back into Looker. 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 Looker dashboard event or Redshift schema change and trigger actions across your entire analytics stack.
When a Looker-generated query takes longer than expected to run in Redshift.
When a user opens a Look or dashboard, or when a scheduled delivery completes.
When developers commit changes to explores, views, or dimension definitions in production.
Generate PDTs in Looker based on Redshift query patterns or data availability.
Invalidate and rebuild Looker query cache when underlying Redshift data changes.
Create or update Looker dashboards showing usage stats, query performance, or data quality.
When columns are added, removed, or changed in Redshift tables used by LookML models.
When ETL jobs don't update Redshift tables on schedule or expected row counts aren't met.
When Redshift queries consume unusual compute resources or scan excessive data volumes.
Create tables, add sort/distribution keys, or apply vacuum operations based on BI usage patterns.
Execute data quality checks or metric reconciliation queries against Redshift tables.
Adjust WLM configuration or query priorities based on Looker dashboard importance.
See how Redbird AI can sync Looker with Redshift and automate the validation, monitoring, and optimization work your data team does manually today.