Redbird AI automatically syncs your Kafka event streams into Looker dashboards and reports. Stop writing custom ETL scripts to land streaming data into your warehouse, or waiting on engineering to expose real-time metrics to business teams.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically route high-volume event streams from Kafka topics into your data warehouse and refresh Looker explores and dashboards. Business teams see live metrics without engineering building custom pipelines for every new event type.
Monitor Looker query logs and user activity to detect anomalous patterns, null spikes, or metric inconsistencies. Automatically publish alerts to Kafka topics that feed operational monitoring systems and trigger incident response workflows.
Intercept raw clickstream and user behavior events from Kafka, enrich with LookML dimension mappings and business logic, then write enhanced events back for consistent analytics. Ensure streaming data matches your governed metric definitions from day one.
Trigger scheduled or ad-hoc Looker reports when specific event patterns appear in Kafka streams—like transaction thresholds, system errors, or customer milestones. Deliver context-rich analytics to stakeholders the moment something important happens.
Stream user interactions with embedded Looker dashboards—drill-downs, filter changes, export actions—into Kafka for real-time product analytics. Understand how customers use your analytics features without custom instrumentation.
Batch Kafka log and sensor data into optimized warehouse tables on schedule, then automatically update or create LookML explores with appropriate dimensions and measures. Turn operational data streams into queryable analytics assets without manual modeling.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Kafka 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 Kafka's event schemas, topic partitioning, and consumer groups alongside Looker's LookML semantic layer, explores, and dashboard structure.
Redbird automatically maps Kafka event schemas—JSON, Avro, Protobuf—to LookML dimensions and measures, suggesting appropriate aggregations and join logic. It detects schema evolution in your streams and proposes LookML updates to match. When streaming data lands in your warehouse, Redbird ensures it aligns with your governed metric definitions, maintaining consistency between real-time pipelines and self-serve analytics.
faster than building custom streaming ETL and updating LookML models manually
Redbird can pull from Kafka 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 Kafka 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 Kafka into Looker, or from Looker back into Kafka. 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 from any event in your Kafka streams or any activity in Looker—Redbird handles the orchestration across both systems.
Trigger when messages arrive in specified Kafka topics, with filtering by partition, key pattern, or message content.
Detect when consumer group lag crosses defined thresholds, indicating processing delays or capacity issues.
Trigger when new schema versions are registered or existing schemas evolve in your schema registry.
Write structured messages to Kafka topics with specified keys, headers, and partition routing.
Collect and batch Kafka messages, then write to data warehouse tables optimized for Looker querying.
Modify topic retention, partition count, or configuration settings based on usage patterns or data volume.
Trigger when specific Looker dashboards are accessed, scheduled, or delivered to users or groups.
Detect when Looker queries finish running, including execution time, row counts, and query patterns.
Trigger when Looker alert thresholds are crossed based on scheduled query results or metric anomalies.
Execute Looker looks or dashboard queries and deliver results via email, Slack, or other channels.
Programmatically modify explores, dimensions, or measures in LookML to reflect new data sources or business logic.
Trigger rebuild of Looker PDTs or materialized views when upstream Kafka data patterns change.
Sync Kafka event streams with Looker analytics in minutes. Redbird AI handles the schema mapping, data orchestration, and warehouse optimization so your team can focus on insights, not infrastructure.