In the era of real-time applications, data must flow instantly from producers to analytics engines and storage systems. Apache Pulsar, with its event-driven architecture, is built to handle high-throughput streaming data. When combined with NoSQL databases like MongoDB, Apache Cassandra, or Amazon DynamoDB, Pulsar can power robust, scalable, and highly responsive pipelines.

This blog dives into technical best practices for integrating Pulsar with NoSQL databases. We’ll explore connectors, change data capture (CDC), schema handling, and real-world examples that help bridge messaging with flexible, distributed data stores.

Why Combine Pulsar and NoSQL

Apache Pulsar excels in decoupling systems through asynchronous messaging, while NoSQL databases provide schema-less, horizontally scalable storage with low-latency reads/writes. Together, they offer:

  • Real-time ingestion from microservices, IoT, or logs
  • Scalable persistence of semi-structured documents or key-value pairs
  • Flexible querying and analytics for downstream consumers
  • Event replay capabilities for rebuilding states or troubleshooting

Integration Architecture Overview

A typical pipeline involves:

[ Producers ] → [ Apache Pulsar Topics ] → [ Pulsar IO Connector ] → [ NoSQL Database ]
  • Producers: Microservices, Kafka Bridges, IoT sensors, etc.
  • Topics: Pulsar topics where events are published
  • Pulsar IO Connectors: Prebuilt sink connectors to push data to NoSQL systems
  • NoSQL Store: MongoDB, Cassandra, DynamoDB, etc.

Supported NoSQL Connectors

Apache Pulsar’s Pulsar IO framework offers out-of-the-box connectors for many NoSQL databases.

NoSQL Database Connector Type Notes
MongoDB Sink Inserts or updates documents
Cassandra Sink Writes events to CQL tables
DynamoDB Sink Uses AWS SDK (via AWS Pulsar Sink)
Couchbase Sink Compatible with JSON documents

To install a connector:

bin/pulsar-admin connectors create \
--archive connectors/pulsar-io-mongo-2.10.4.nar \
--name mongo-sink \
--sink-config-file mongo-sink-config.json

MongoDB Integration Example

Step 1: Configure mongo-sink-config.json

{
"mongoUri": "mongodb://user:pass@mongo-host:27017",
"database": "realtime_db",
"collection": "events",
"batchSize": 100,
"batchTimeMs": 500
}

Step 2: Launch the sink

bin/pulsar-admin sinks create \
--name pulsar-mongo \
--sink-type mongo \
--inputs my-topic \
--sink-config-file mongo-sink-config.json \
--tenant public \
--namespace default

Note: Messages should be in JSON format to match MongoDB’s document model.

Cassandra Integration Example

Create a compatible schema:

CREATE TABLE sensor_data (
sensor_id text PRIMARY KEY,
timestamp bigint,
temperature float,
humidity float
);

Configure the sink:

{
"roots": "127.0.0.1",
"keyspace": "pulsar_ks",
"columnFamily": "sensor_data",
"username": "cassandra",
"password": "cassandra"
}

DynamoDB Integration Example

Example config using the AWS Pulsar connector:

{
"awsRegion": "us-east-1",
"awsAccessKeyId": "YOUR_KEY",
"awsSecretAccessKey": "YOUR_SECRET",
"tableName": "IoTEvents"
}

Deploy the sink:

bin/pulsar-admin sinks create \
--archive pulsar-io-aws-dynamodb.nar \
--name dynamo-sink \
--sink-config-file dynamodb-config.json \
--inputs iot-topic

Schema and Compatibility Considerations

Use Pulsar’s schema registry for strong schema enforcement:

Schema<UserEvent> schema = Schema.JSON(UserEvent.class);
Producer<UserEvent> producer = client.newProducer(schema)
.topic("persistent://public/default/user-events")
.create();

Tips:

  • Use JSONSchema or AvroSchema for compatibility
  • Enable forward/backward schema versioning
  • Align message structure with NoSQL document/table models

Real-Time Analytics Use Case

Example: Building a real-time dashboard using MongoDB:

  1. Backend services publish events to Pulsar
  2. Pulsar Mongo Sink stores them in a MongoDB collection
  3. A frontend dashboard queries this collection for visualization
  4. Optional: Pulsar Functions can enrich data before storage

Ideal for: e-commerce analytics, IoT monitoring, real-time financial insights.

Performance and Tuning Tips

  • Enable batch writes in the sink configuration
  • Use partitioned topics for parallelism
  • Align Pulsar ack timeouts with sink write latencies
  • Ensure NoSQL storage scaling matches throughput

Security Considerations

  • Enable TLS encryption for Pulsar and DB connections
  • Use JWT authentication and role-based access control
  • Store secrets in Kubernetes Secrets or external vaults
  • Restrict sink permissions by namespace scope

Conclusion

Apache Pulsar’s built-in support for NoSQL connectors and schema enforcement makes it an ideal platform for building real-time data pipelines. Whether you’re logging IoT telemetry to MongoDB, persisting user actions in Cassandra, or ingesting retail events into DynamoDB, Pulsar enables scalable, low-latency, and flexible event delivery.

For modern, distributed architectures where speed, scalability, and simplicity matter, Pulsar + NoSQL is a proven, production-grade combination.