As businesses move to the cloud and embrace real-time data architectures, traditional batch ETL systems are no longer sufficient. Apache Pulsar, a cloud-native messaging and event streaming platform, offers a powerful foundation for building scalable, real-time ETL and stream processing pipelines in the cloud.

In this blog, we’ll explore how to leverage Apache Pulsar for stream processing and ETL workloads across modern cloud platforms. We’ll cover integration patterns, architecture components, and deployment best practices for building reliable, scalable data pipelines.

Why Pulsar for Cloud-Based ETL and Stream Processing?

Apache Pulsar offers key advantages for ETL and real-time analytics in the cloud:

  • Multi-tenancy for organizing ETL jobs by team or application
  • Built-in geo-replication for hybrid/multi-cloud architectures
  • Decoupled compute and storage for independent scaling
  • Serverless Pulsar Functions for inline transformation
  • Native support for streaming and queuing workloads

These features make Pulsar ideal for cloud-based ETL, where elasticity and fault tolerance are critical.

Pulsar in a Cloud ETL Architecture

[Data Sources: APIs, Sensors, Logs, Databases]
↓
[Producers / Kafka Connect / Pulsar IO]
↓
[Pulsar Topics]
↓
[Pulsar Functions / Flink / Spark / NiFi / Beam]
↓
[Transformed Pulsar Topics or Data Sinks]
↓
[Cloud Storage / Databases / Data Warehouses]

Apache Pulsar acts as the data backbone, decoupling ingestion from transformation and storage.

Ingesting Data with Pulsar IO

Pulsar IO provides built-in connectors for ingesting from and writing to external systems.

Examples:

  • Sources: JDBC, Kafka, MQTT, AWS S3, Kinesis
  • Sinks: Elasticsearch, Cassandra, BigQuery, Redshift, Snowflake

To deploy a JDBC source connector:

bin/pulsar-admin sources create \
--archive pulsar-io-jdbc-source.nar \
--tenant public \
--namespace default \
--name mysql-source \
--destination-topic-name db-events \
--source-config-file mysql-config.yaml

This makes Pulsar ideal for change data capture (CDC) and ingesting events from cloud-native sources.

Real-Time Transformation with Pulsar Functions

Pulsar Functions are lightweight, serverless compute units that run within Pulsar and apply real-time transformations to streaming data.

Example: Convert temperature from Fahrenheit to Celsius

def convert_temp(input, context):
data = json.loads(input)
data['temp_c'] = (data['temp_f'] - 32) * 5 / 9
return json.dumps(data)

Deploy via CLI or REST:

bin/pulsar-admin functions create \
--tenant public \
--namespace default \
--name temp-converter \
--inputs temp-fahrenheit \
--output temp-celsius \
--py convert_temp.py \
--classname convert_temp

Pulsar Functions reduce ETL latency and infrastructure overhead by processing events in-stream.

For complex workflows, integrate Pulsar with powerful stream processors:

  • Apache Flink: Use pulsar-flink-connector for windowed aggregations and joins
  • Apache Spark Structured Streaming: Use pulsar-spark to read/write topics
  • Apache Beam: Run ETL pipelines across GCP, AWS, or Kubernetes

Example Flink job to count messages per device:

DataStream<String> stream = env
.addSource(new PulsarSource<>(...));

stream
.map(record -> extractDeviceId(record))
.keyBy(id -> id)
.window(TumblingProcessingTimeWindows.of(Time.minutes(1)))
.sum("count")
.addSink(new PulsarSink<>(...));

Writing to Cloud Sinks

You can push transformed data to cloud-native sinks:

  • Amazon S3 / GCS for raw and transformed logs
  • BigQuery / Redshift / Snowflake for analytics
  • Elasticsearch for real-time search and dashboards
  • PostgreSQL / DynamoDB for enriched operational data

Example: Writing to S3 via Pulsar IO

awsS3BucketName: my-bucket
awsRegion: us-east-1
fileFormat: json
compressionType: gzip
batchSize: 500

Cloud-Native Deployment Options

You can deploy Pulsar on:

  • Kubernetes via Pulsar Helm Charts
  • Confluent Cloud or StreamNative Cloud
  • Amazon EKS, GKE, or AKS for managed cloud orchestration
  • Serverless Functions (Pulsar Functions or AWS Lambda via sink connectors)

Use Helm to install Pulsar in minutes:

helm repo add apache https://pulsar.apache.org/charts
helm install pulsar apache/pulsar --set initialize=true

Best Practices

  • Partition topics by tenant or workload type to isolate pipelines
  • Use Key_Shared subscriptions for parallel processing with ordering
  • Monitor processing lag and throughput with Prometheus/Grafana
  • Apply backpressure handling in Flink/Spark to prevent memory overload
  • Enable TLS + token-based auth to secure ETL pipelines end-to-end

Conclusion

Apache Pulsar is a powerful platform for enabling real-time stream processing and ETL in modern cloud environments. Its modular architecture, rich ecosystem, and seamless integration with external systems make it ideal for building low-latency, cloud-native data pipelines.

Whether you’re streaming CDC data to BigQuery or transforming logs before indexing in Elasticsearch, Pulsar provides the scalability and reliability required for modern cloud-scale ETL.