Redbird AI syncs dbt model changes to Power BI automatically — no more manually updating semantic models, refreshing dataset schemas, or copying documentation between systems. Keep your dashboards aligned with your transformation layer in real time.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When dbt models are updated or rebuilt, automatically propagate column definitions, data types, and lineage information to corresponding Power BI datasets. Business users see up-to-date field descriptions and relationships without analytics engineers manually syncing metadata across tools.
Automatically refresh Power BI datasets when upstream dbt models finish running successfully in your data warehouse. Ensures dashboards reflect the latest transformed data without manual coordination between transformation pipelines and BI refresh schedules.
Convert dbt semantic layer metrics into native Power BI measures with correct DAX syntax and aggregation logic. Maintains consistency between how metrics are defined in your transformation layer and calculated in reports, eliminating metric discrepancies across tools.
Monitor Power BI dataset errors and trace them back to upstream dbt model changes. When column renames or type changes break downstream dashboards, Redbird identifies the dbt commit responsible and notifies the relevant team members with full context.
Capture which Power BI reports and dashboards consume each dbt model, then write that usage information back to dbt model documentation. Analytics engineers understand downstream impact before making breaking changes to transformation logic.
Scan Power BI workspaces to identify all reports and datasets built on your data warehouse, then create or update dbt exposure YAML files documenting these downstream dependencies. Keeps your dbt project documentation current with actual BI tool usage without manual YAML editing.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize dbt and Power BI 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 dbt's transformation layer semantics and Power BI's semantic modeling structure, intelligently mapping between SQL-based analytics engineering workflows and business intelligence consumption patterns.
Redbird parses dbt manifest files, model YAML definitions, and compiled SQL to understand your transformation logic, column lineage, and metric calculations. It simultaneously interprets Power BI dataset schemas, table relationships, DAX measures, and report dependencies. This dual understanding enables automatic metadata synchronization, intelligent schema migration, and impact analysis across your analytics stack without fragile field-level mapping configurations.
faster than updating Power BI datasets manually after dbt schema changes
Redbird can pull from dbt and Power BI 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 dbt or Power BI.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from dbt into Power BI, or from Power BI back into dbt. 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 dbt model runs, test results, or Power BI dataset refresh events — Redbird connects both sides of your analytics workflow.
Fires when a specific dbt model or set of models finishes executing successfully in your data warehouse.
Triggers when data quality tests fail on any model, capturing test type and failure details.
Detects when column names, types, or table structure changes in compiled dbt models between runs.
Write or append content to dbt model YAML files including descriptions, tags, and meta properties.
Generate or update exposure YAML files documenting downstream BI dependencies and report consumers.
Apply tags or custom meta properties to models based on downstream consumption patterns or lineage.
Fires when a scheduled or on-demand dataset refresh finishes, with success or failure status.
Triggers when column mismatches, type errors, or missing tables break dataset refresh operations.
Detects when reports are created or updated in monitored workspaces, capturing underlying dataset connections.
Trigger immediate dataset refresh via XMLA endpoint or Power BI REST API after upstream data updates.
Modify table structures, column metadata, and relationships in semantic models to match upstream changes.
Generate DAX measure definitions from metric specifications, maintaining calculation consistency across tools.
Stop manually syncing schemas between dbt and Power BI. Redbird keeps your transformation layer and BI platform aligned automatically, so analytics engineers can ship faster and dashboards stay accurate.