Redbird AI automates the flow between your event streams and operational database. Stop writing custom Kafka consumers, manually managing offset tracking, or building bespoke ETL scripts to persist streaming data into MongoDB collections.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Automatically consume events from Kafka topics and write them to MongoDB collections with intelligent schema mapping. Redbird handles batching, error handling, and idempotency without custom consumer code. Perfect for building queryable operational data stores from streaming events.
Capture MongoDB change stream events and publish them to Kafka topics for real-time replication, CDC workflows, or event-driven architectures. Redbird transforms MongoDB oplog entries into structured Kafka messages with configurable partitioning strategies.
Look up enrichment data from MongoDB collections as Kafka events arrive, then write the augmented records back to MongoDB or downstream topics. Ideal for adding customer profiles, product metadata, or configuration data to raw event streams in real time.
Automatically batch and compress Kafka messages into time-bucketed MongoDB documents for long-term retention. Redbird optimizes storage patterns and creates indexes for efficient time-range queries, reducing storage costs while maintaining queryability.
Compare real-time Kafka event metrics against historical patterns stored in MongoDB to detect anomalies. Redbird AI automatically calculates baselines, identifies deviations, and triggers alerts when stream behavior changes unexpectedly.
Run scheduled MongoDB aggregation pipelines and publish results as structured events to Kafka topics for consumption by analytics systems, dashboards, or downstream services. Automate daily rollups, user cohort analyses, or compliance reports without custom scripts.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Kafka 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 AI understands Kafka's message schemas, partition strategies, and offset management alongside MongoDB's document structures, indexes, and aggregation pipelines—no manual mapping required.
Redbird automatically detects Avro, JSON, and Protobuf schemas in your Kafka topics and maps them to MongoDB document structures. It infers optimal shard keys from Kafka partition keys, handles nested event payloads without flattening, and suggests appropriate MongoDB indexes based on your query patterns. The AI adapts to schema evolution in both systems, automatically handling new fields, type changes, and backward compatibility.
faster than building custom Kafka consumers with MongoDB drivers
Redbird can pull from Kafka 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 Kafka 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 Kafka into MongoDB, or from MongoDB 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 automations from new Kafka messages, consumer lag alerts, MongoDB document changes, or any database operation—Redbird connects both systems bidirectionally.
Trigger when a new message is published to any Kafka topic, with filters by partition, key pattern, or message content.
Detect when consumer lag grows beyond a specified threshold, indicating processing delays or capacity issues.
Monitor when topics are repartitioned or new partitions are added, enabling automatic rebalancing workflows.
Write structured messages to any Kafka topic with configurable partitioning, keys, and headers.
Modify topic settings including retention policies, compaction settings, and replication factors programmatically.
Programmatically reset consumer offsets to replay messages or skip to latest positions during incident recovery.
Trigger on any insert, update, or replace operation in specified MongoDB collections using change streams.
Monitor the output of aggregation pipelines and trigger when computed metrics cross thresholds or change significantly.
Alert when a MongoDB collection grows beyond storage thresholds or document count limits.
Write single documents or bulk insert batches into MongoDB collections with automatic schema validation.
Execute complex aggregation queries to transform, group, or analyze documents and use results in downstream workflows.
Modify multiple documents matching filter criteria with field updates, increments, or array operations.
Join data teams using Redbird AI to sync Kafka and MongoDB without writing consumer code or managing infrastructure. Set up your first event-to-database workflow in minutes.