As data volumes and velocity surge across modern enterprises, traditional batch ETL pipelines struggle to meet the demands of real-time analytics and operational agility. Enter Apache Pulsar — a cloud-native messaging and event-streaming platform that enables continuous data integration into data lakes and lakehouse architectures.

This post explores how Pulsar helps build scalable, real-time data ingestion pipelines, decouples sources from sinks, supports schema evolution, and delivers streaming ETL to transform raw data into analytics-ready formats.

Why Use Apache Pulsar for Data Lake Integration?

Apache Pulsar offers key advantages for continuous data integration:

  • Decoupled compute and storage for flexible scaling
  • Multi-tenancy and isolation across pipelines
  • Built-in schema registry for data consistency
  • Support for streaming ETL using Pulsar Functions and IO connectors
  • Seamless integration with object stores, warehouses, and analytics tools

By acting as a central message bus, Pulsar allows multiple producers and consumers to work independently — reducing operational coupling between systems.

Data Lake Ingestion Architecture with Pulsar

[Databases / APIs / IoT / Logs]
↓
[Pulsar Producers]
↓
[Pulsar Topics]
↓
[Stream Processing / Pulsar Functions]
↓
[Data Lake (e.g., S3 / ADLS / HDFS)]
↓
[Query Engines: Presto / Trino / Athena]

This architecture allows for real-time data flow into your lake, enabling near-instant analytics and continuous model updates.

Ingesting Data from Source Systems

Use Pulsar IO connectors or custom producers to bring data into Pulsar from:

  • Relational databases (via CDC tools like Debezium)
  • NoSQL stores
  • RESTful APIs
  • Log streams (Syslog, FluentBit)
  • IoT devices and sensors

Example: Connect MySQL CDC into Pulsar

{
"tenant": "data",
"namespace": "integration",
"name": "mysql-cdc-source",
"archive": "connectors/pulsar-io-debezium-mysql.nar",
"topicName": "mysql-orders",
"configs": {
"database.hostname": "mysql.internal",
"database.port": "3306",
"database.user": "replicator",
"database.password": "password",
"database.server.name": "orders",
"database.whitelist": "ecommerce"
}
}

Stream Processing and Transformation

Transform and enrich incoming data before it hits the data lake using:

  • Pulsar Functions
  • Apache Flink
  • Apache Spark Structured Streaming

Example: Pulsar Function to filter and enrich events

def process(input, context):
event = json.loads(input)
if event["status"] == "completed":
event["processed_at"] = context.get_current_message_topic_name()
return json.dumps(event)

Storing in Data Lake Destinations

Write transformed data to:

  • Amazon S3 / Azure Data Lake / HDFS
  • Delta Lake or Apache Iceberg
  • Parquet / Avro / ORC file formats

Use Pulsar’s Sinks or integrate via Apache NiFi, Kafka Connect (via MirrorMaker), or custom Flink/Spark jobs.

Example: Configure Pulsar IO sink to S3

{
"archive": "connectors/pulsar-io-s3.nar",
"topicName": "processed-orders",
"name": "s3-sink",
"tenant": "data",
"namespace": "integration",
"configs": {
"bucketName": "my-data-lake",
"region": "us-east-1",
"accessKeyId": "xxx",
"secretAccessKey": "yyy",
"formatType": "parquet",
"batchSize": "500"
}
}

Schema Management and Evolution

Apache Pulsar supports schema enforcement at the topic level using Avro, JSON, Protobuf.

  • Producers register schema automatically
  • Consumers can enforce compatibility (BACKWARD, FORWARD, FULL)

Benefits:

  • Prevents schema drift
  • Ensures downstream systems receive consistent data
  • Enables schema evolution without downtime

Real-Time Use Cases

  • E-Commerce: Sync order, cart, and user data into a real-time lake for personalization
  • IoT: Stream device metrics and anomalies into the lake for monitoring and ML
  • Finance: Ingest transaction logs for fraud detection and compliance
  • Healthcare: Collect patient vitals and EHR updates for live dashboards

Best Practices

  • Partition topics based on volume (e.g., by region or customer ID)
  • Use batching and compression for high-volume streams
  • Implement backpressure and retry handling in Pulsar Functions
  • Use Schema Registry for versioning and enforcement
  • Monitor ingestion with Prometheus / Grafana / Pulsar Manager

Conclusion

Apache Pulsar makes it easy to build real-time, scalable, and flexible data integration pipelines for data lakes. With built-in support for schema management, streaming transformation, and multi-sink delivery, Pulsar is a powerful engine for modern lakehouse architectures.

By adopting Pulsar, you can break free from batch ETL, reduce pipeline complexity