Redbird AI syncs data between your lakehouse and document database automatically. Stop writing custom ETL scripts to move training data, model outputs, and feature store updates between Databricks and MongoDB—let AI handle the schema mapping and transformations.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically push batch inference results and model scores from Databricks workflows into MongoDB documents. Redbird maps nested predictions to your application schema and handles incremental updates, so your product teams always work with fresh model outputs.
Stream product events, user interactions, and application logs from MongoDB into Databricks Delta tables. Redbird flattens document structures into analytics-ready formats, preserving nested fields and array data for feature engineering pipelines.
Automatically sync feature values from your Databricks Feature Store back to MongoDB collections. When features are updated or recalculated, Redbird pushes the latest embeddings, aggregations, and derived metrics to power real-time application logic.
Migrate historical MongoDB documents into Delta Lake on a schedule. Redbird handles schema evolution, converts BSON types to Spark-native formats, and partitions data by time for efficient querying in Databricks SQL and notebooks.
Start ETL pipelines, model retraining, or data quality checks in Databricks whenever specific documents are inserted or updated in MongoDB. Redbird watches collections for pattern matches and kicks off workflows with relevant context.
Keep MongoDB collections in sync with curated Delta tables from Databricks. When your data engineering team updates gold-layer tables, Redbird automatically reflects those changes in MongoDB to serve low-latency reads for applications and APIs.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Databricks and MongoDB 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 Spark DataFrame schemas and MongoDB document structures—so you can automate data movement without wrestling with type mismatches or nested array conversions.
Redbird reads Databricks table schemas—including complex types, maps, and structs—and intelligently maps them to MongoDB document formats. It handles BSON type conversions, nested array flattening, and schema evolution automatically. Whether you're syncing inference results to collections or pulling event logs into Delta, Redbird preserves data fidelity and adapts to changes in either system without breaking pipelines.
faster than building custom MongoDB connectors for Databricks jobs
Redbird can pull from Databricks and MongoDB 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 Databricks or MongoDB.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from Databricks into MongoDB, or from MongoDB back into Databricks. 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 Databricks job completion or MongoDB document event—Redbird connects the dots across your lakehouse and operational database.
Trigger workflows when a specific Databricks job or notebook finishes running successfully or fails.
Detect when new data is written to a Delta table or when existing records are modified.
Start syncs when a new model version is registered or transitioned to production in MLflow.
Insert or upsert rows into a Delta table with automatic schema merging and partition handling.
Execute a specific notebook with parameters passed from MongoDB events or upstream workflows.
Push computed feature values into Databricks Feature Store for model training or serving.
Trigger when a new document is added to a MongoDB collection, with optional field-level filters.
Detect changes to existing documents based on field values or update timestamps.
Start workflows when a collection grows beyond a certain document count or size.
Write new documents to a MongoDB collection with automatic field mapping from Databricks output.
Modify existing documents matching a filter, merging in new fields from Databricks results.
Insert or update documents based on a unique identifier, ensuring idempotent syncs from Delta tables.
See how Redbird AI syncs Databricks and MongoDB in minutes. Stop maintaining custom connectors and start automating the data flows between your lakehouse and operational database.