Redbird AI automates the data pipeline between your S3 data lake and MongoDB operational database. Stop manually extracting files, transforming formats, and loading documents — let AI handle the sync, schema mapping, and validation between object storage and NoSQL collections.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When new JSON or NDJSON files land in S3 buckets, Redbird validates structure, maps fields to your MongoDB schema, and inserts documents into the target collection. Handles batching, deduplication, and error logging without custom Lambda functions.
Schedule or trigger exports of entire MongoDB collections or filtered subsets to S3 as compressed JSON or Parquet files. Maintain versioned backups in your data lake with proper partitioning and metadata tagging for long-term retention.
Transform structured CSV or Parquet files stored in S3 into MongoDB documents with automatic type inference and nested field mapping. Perfect for loading external datasets, partner feeds, or batch processing results into your operational database.
Capture real-time database changes using MongoDB change streams and write them to partitioned S3 paths as immutable event logs. Enables downstream analytics, CDC pipelines, and integration with Spark or data warehouse ingestion.
When documents are inserted or updated in MongoDB, Redbird pulls enrichment data from CSV or JSON lookup files in S3, merges attributes, and updates the document. Maintains fresh reference data without embedding static files in your application.
Validate file structure, required fields, and data types in S3 objects before attempting MongoDB insertion. Send alerts when files fail validation rules, preventing corrupt data from entering your operational database.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Amazon S3 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 S3's unstructured file storage patterns and MongoDB's flexible document schema, automating the translation between object storage and NoSQL collections.
Redbird automatically infers schema from JSON, CSV, and Parquet files in S3, then maps them to MongoDB collection structures with proper type casting. It handles nested objects, arrays, and BSON types natively, understanding how to flatten or preserve hierarchies based on your use case. Whether you're loading event logs, syncing reference data, or archiving collections, Redbird adapts to your bucket structure and database schema without brittle scripts.
faster than building Lambda ETL functions for S3-to-MongoDB pipelines
Redbird can pull from Amazon S3 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 Amazon S3 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 Amazon S3 into MongoDB, or from MongoDB back into Amazon S3. 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 S3 bucket event or MongoDB collection change, then take action across your entire data infrastructure.
Trigger when files are added to specific S3 buckets or prefix paths.
Detect files matching naming conventions or extensions like .json, .csv, or .parquet.
React when S3 object tags, metadata, or storage class changes.
Fetch S3 objects and parse JSON, CSV, Parquet, or compressed formats.
Upload files or serialized data to S3 with custom naming and partitioning.
Replicate or relocate S3 objects based on processing status or data classification.
Trigger when new documents are added to specified MongoDB collections.
Detect changes to existing documents using change streams or polling.
Monitor collection document counts or data volume for archival triggers.
Write single or batch documents to MongoDB with automatic _id generation.
Modify existing documents matching filter criteria with new field values.
Run aggregation pipelines or find queries and extract results for downstream use.
Stop building custom ETL scripts between S3 and MongoDB. Redbird AI connects your cloud storage to your database with intelligent automation that adapts to your data structures.