Stop manually extracting files from blob containers, transforming raw data, and updating BI datasets. Redbird AI automatically syncs Azure Blob Storage with Looker, so your analytics stay current without engineering overhead or custom scripts.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When new structured data files land in Azure Blob Storage containers, Redbird detects schema, transforms the data, and loads it into your data warehouse where Looker models can immediately query it. No manual ETL pipelines or file monitoring scripts required.
Automatically process application logs, server logs, or event streams stored in blob containers. Redbird extracts metrics and KPIs, then updates the underlying tables that power your Looker operational dashboards without manual log parsing.
Schedule Looker Looks and explores to run automatically, then archive the results as structured files in Azure Blob Storage. Perfect for compliance requirements, historical snapshots, or feeding data science workflows with governed BI metrics.
When Looker dashboards detect anomalies or data quality issues, automatically tag the source files in Azure Blob Storage with validation status and error details. Creates a feedback loop between BI observations and raw data lineage.
Monitor incoming files in blob containers for schema changes, missing columns, or unexpected value ranges. Redbird compares against LookML model requirements and alerts data teams before broken pipelines impact executive reporting.
Treat Azure Blob Storage as your data lake landing zone. When third-party vendors, APIs, or batch processes drop files into designated containers, Redbird orchestrates the transformation and loading into staging tables that Looker PDTs and persistent derived tables depend on.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Azure Blob Storage 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 AI understands both Azure Blob Storage file structures and Looker's LookML semantic layer, automatically mapping between raw data formats and governed BI metrics.
Redbird analyzes file formats in your Azure containers—CSV headers, JSON schemas, Parquet column types—and maps them to the dimensions and measures defined in your LookML models. It detects when new blob files match existing Looker explore patterns, validates data types against your semantic layer, and flags mismatches before they break dashboards. No manual schema mapping or brittle column-name matching required.
faster than building custom Azure Functions and dbt pipelines
Redbird can pull from Azure Blob Storage 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 Azure Blob Storage 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 Azure Blob Storage into Looker, or from Looker back into Azure Blob Storage. 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 file event in Azure Blob Storage or any query, dashboard, or data event in Looker.
Fires when a new blob is uploaded to a specified container or path prefix.
Triggers when an existing blob is updated or overwritten with new content.
Detects files matching specific naming patterns like date stamps or vendor identifiers.
Write processed data back to Azure Blob Storage as CSV, JSON, or Parquet files.
Move blobs to archive or cool tiers based on age or Looker usage patterns.
Add custom metadata like processing status, validation results, or lineage information.
Fires when a scheduled Looker report or dashboard finishes generating results.
Triggers when Looker data tests detect anomalies, nulls, or unexpected value ranges.
Detects when key metrics or dimensions in a Looker dashboard cross thresholds or show new categories.
Trigger a rebuild of persistent derived tables or aggregate awareness tables in Looker.
Automatically generate saved Looks from dynamic query results or exploration outputs.
Programmatically modify view files or model definitions based on new data sources.
Stop building and maintaining custom pipelines between Azure Blob Storage and Looker. Redbird AI automates the data work so your team can focus on insights, not infrastructure.