Redbird AI automates the pipeline between your Azure data lake and Google's analytics warehouse. Stop writing custom ETL scripts, manually transferring files, or building fragile connectors to sync unstructured data into structured tables.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When CSV, JSON, Parquet, or Avro files land in Blob Storage containers, Redbird automatically ingests them into the correct BigQuery dataset and table. Schema detection, type mapping, and partition handling happen without manual intervention.
Schedule or trigger BigQuery analytical queries and write results directly to Azure Blob containers as compressed files. Perfect for cross-cloud backup, compliance archiving, or feeding downstream Azure-based applications.
Continuously monitor Blob Storage for new event logs, application telemetry, or IoT data files. Redbird streams each batch into BigQuery tables, enabling near-real-time dashboards and anomaly detection on Azure-generated data.
Apply AI-powered validation, deduplication, and enrichment to files in Azure Blob before warehouse ingestion. Redbird checks schemas, flags quality issues, appends metadata, and only loads clean records into BigQuery.
After running ML inference in BigQuery, export scored datasets to Azure Blob Storage as structured files. This enables Azure applications, Power BI, or downstream services to consume predictions without direct BigQuery access.
Monitor file arrival patterns and ingestion status across the pipeline. Redbird detects missing files, schema drift, or load failures and sends contextual alerts to Slack or email with error details and suggested fixes.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Azure Blob Storage and BigQuery 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 both Azure Blob Storage file structures and BigQuery table schemas, so you can sync unstructured data into analytics-ready tables without writing transformation code.
Redbird maps Blob Storage file formats — CSV, JSON, Parquet, Avro, ORC — to BigQuery column types, nested structures, and partitioning schemes automatically. The AI detects schema evolution, handles type mismatches, and suggests optimal table designs based on file patterns. When new columns appear in your Azure files, Redbird adjusts BigQuery schemas without breaking existing queries or requiring manual DDL changes.
faster than building custom Azure-to-GCP ETL pipelines
Redbird can pull from Azure Blob Storage and BigQuery 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 BigQuery.
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 BigQuery, or from BigQuery 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 query completion in BigQuery, then take action across both platforms.
Fires when any file or blob is added to a specified Azure Blob Storage container or folder path.
Detects when an existing blob is updated, replaced, or has metadata changed in Azure storage.
Triggers when multiple files matching a pattern land in a container within a defined period.
Write data or query results as a new blob in a specified Azure container with custom naming.
Transfer files across Azure Blob containers or storage accounts for staging or archival workflows.
Remove files from hot storage or move them to archive tier based on age or processing status.
Fires when a BigQuery scheduled query finishes running, making results available for export or further processing.
Detects when data is appended to a BigQuery table via streaming insert, batch load, or query insert.
Triggers when columns are added, modified, or removed from a BigQuery table schema definition.
Insert rows from external sources into a specified dataset and table with schema auto-detection or mapping.
Execute a custom SQL query in BigQuery and write results to a destination table or external storage.
Manage partitioned tables by creating new date or range partitions or updating partition metadata.
Join data teams using Redbird AI to automate Azure-to-BigQuery pipelines. Stop writing ETL glue code and start moving data between your Azure data lake and Google Cloud warehouse in minutes.