Redbird AI bridges your S3 data lake and Looker dashboards automatically. Stop manually tracking file uploads, copying data between storage and warehouse, or waiting for engineering to refresh analytics datasets. Let AI handle the orchestration.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When ETL jobs deposit processed files in S3, automatically load them into your warehouse and trigger Looker model refreshes. Keep dashboards current without manual intervention or scheduler fragility.
Automatically save Looker dashboard exports, scheduled Look results, and large query outputs to S3 for long-term storage. Maintain historical snapshots of key metrics and comply with data retention policies.
Monitor S3 buckets for new raw data arrivals from third-party vendors or internal systems. Notify Looker developers when source data is available, triggering LookML model updates and validation workflows.
Pull dimension tables and metric definitions from Looker's semantic layer and join them with raw event data in S3. Create enriched datasets that combine operational logs with business intelligence context.
Validate file schemas, row counts, and data freshness in S3 before allowing warehouse loads. Cross-reference with Looker usage patterns to ensure critical dashboards always have complete, accurate source data.
Query historical data stored in S3 alongside current metrics from Looker's semantic layer. Build comprehensive executive reports that span years of archived data without loading everything into your warehouse.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Amazon S3 and Looker 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 S3 bucket structures, file formats, and partition schemes alongside Looker's LookML models, explores, and persistent derived tables—so your data lake and analytics layer stay in sync.
Redbird's AI automatically maps S3 file structures—Parquet columns, CSV headers, JSON schemas—to Looker dimensions, measures, and derived tables. It understands bucket naming conventions, partition patterns, and data lake organization, then connects them to your LookML models without custom scripting. When S3 schemas evolve or Looker models change, Redbird adapts the integration automatically.
faster than building S3-to-warehouse-to-Looker pipelines with custom scripts
Redbird can pull from Amazon S3 and Looker 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 Looker.
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 Looker, or from Looker 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 bucket event or Looker dashboard activity—Redbird handles the data translation and orchestration.
Trigger when any file or a file matching specific patterns lands in monitored S3 buckets or prefixes.
Detect when file tags, storage class, or metadata properties change on existing S3 objects.
Respond when objects are removed from S3, triggering cleanup or archival workflows downstream.
Save transformed datasets, reports, or analytics outputs to specified S3 locations with proper partitioning.
Orchestrate data movement across S3 locations based on processing stages or data lifecycle policies.
Modify object properties to track processing status, data lineage, or governance classifications.
Trigger when Looker sends a scheduled report, capturing the results for further processing or archival.
Detect when specific Looker dashboards are viewed, enabling usage tracking and data dependency analysis.
Respond when Looker PDTs complete their refresh cycle, signaling availability of updated aggregated data.
Force Looker to reload specific models or explores after upstream data sources are updated in your warehouse.
Programmatically configure Looker report schedules based on data availability or business event triggers.
Pull current results from Looker dashboards or explores and format them for storage or downstream consumption.
Sync Amazon S3 with Looker in minutes, not sprints. Redbird AI eliminates the pipeline engineering between your data lake and business intelligence layer.