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06 Numbers and Powers

Choosing an Event Streaming Platform for Object Storage

Card No. 06·8532 · Updated · Standard formula, shown below

This guide helps you evaluate and select an event streaming platform (like Apache Kafka, Redpanda, or Pulsar) when your primary workload is object storage—think S3-compatible storage, Azure Blob, or Google Cloud Storage. You’ll learn the specific criteria that matter for event-driven pipelines, data lake ingestion, and change-data-capture from object stores, and you’ll get a step-by-step decision framework. You’ll need a clear picture of your event volume, latency requirements, and existing cloud infrastructure before you start.

What You’ll Need

  • An estimate of your event throughput (events per second) and average event size
  • Your latency requirements (real-time vs. near-real-time vs. batch)
  • Your object storage endpoints and access credentials (for testing)
  • A list of your downstream consumers (analytics, search, ML pipelines)
  • Budget constraints (self-managed vs. managed service)
  • Team expertise with Java/Scala or other JVM languages (common for Kafka)

Step-by-Step

  1. Define your event source and destination. Are you streaming events from object storage (e.g., S3 event notifications) or streaming to object storage (e.g., as a sink)? This determines whether you need strong integration with your storage provider’s event system.
  2. Quantify your throughput and latency. Measure your peak event rate and acceptable end-to-end latency. Object storage workloads often tolerate seconds of delay, but if you need sub-second, you’ll need a platform with low-latency brokers.
  3. List your consumers and their access patterns. Will consumers read in real-time, replay from a point in time, or both? This affects retention and replay capabilities you need.
  4. Evaluate storage-tier integration. Check if the platform has native connectors or sink/source for your object storage. For example, Kafka Connect has S3 sink/source connectors; Pulsar has S3 offloading; Redpanda supports S3 tiered storage.
  5. Compare deployment models. Decide between self-hosted (Kafka, Pulsar, Redpanda) and managed services (Confluent Cloud, Amazon MSK, Redpanda Cloud, StreamNative). Managed reduces ops but may have egress costs.
  6. Assess durability and replication. For object storage workloads, data durability is critical. Ensure the platform supports replication across availability zones and has configurable retention policies that complement your object storage lifecycle.
  7. Check schema and format support. If you use Avro, Parquet, or JSON, verify the platform’s schema registry and serialization support. This is crucial for object storage data lakes.
  8. Test with a proof-of-concept. Set up a small cluster, stream a sample of your actual events to/from object storage, and measure throughput, latency, and failure behavior.
  9. Consider operational tooling. Look for monitoring, alerting, and management UI. For object storage, you’ll want to track lag between event creation and availability.
  10. Review cost structure. Calculate total cost including storage, compute, egress, and any per-event fees. Object storage workloads can be high-volume, so egress costs can dominate.

Common Mistakes

  • Choosing a platform based on popularity rather than fit—Kafka is great but not always the best for object storage if you need simple S3 integration.
  • Underestimating the cost of egress from cloud object storage to the streaming platform. This can be significant.
  • Ignoring retention and compaction needs. If you need to replay events for reprocessing, you need longer retention or tiered storage.
  • Not testing with your actual event size. Small test messages don’t reflect real-world throughput with large objects.
  • Overlooking security and compliance requirements, especially if your object storage contains sensitive data.

Tips

  • Start with a managed service if you lack ops expertise—you can always migrate later.
  • Use tiered storage to reduce costs: keep hot data in the streaming platform, cold data in object storage.
  • Leverage schema registry to avoid data corruption when schemas evolve.
  • Design for idempotent consumers—object storage writes may be retried, so your consumers should handle duplicates gracefully.
  • Monitor end-to-end latency, not just broker metrics. Use a synthetic event to measure the full pipeline.

Troubleshooting

If your proof-of-concept fails to meet expectations:

  • High latency: Check broker configuration (e.g., acks=all), network bandwidth, and consumer group lag. Increase partitions if throughput is low.
  • Data loss: Verify replication factor and min.insync.replicas settings. Ensure your object storage sink is idempotent.
  • Connector errors: Check credentials and permissions for object storage. Ensure the connector has the right IAM roles or access keys.
  • Cost overruns: Review egress costs and consider using a region-local streaming platform to minimize cross-region data transfer.
  • Schema conflicts: Use a schema registry and test schema evolution before deploying.

Frequently Asked Questions

Can I use Apache Kafka directly with S3?

Yes, via Kafka Connect’s S3 sink/source connectors. They handle partitioning and format conversion, but you need to manage the connectors yourself.

Is Pulsar better than Kafka for object storage?

Pulsar has native tiered storage offloading to S3 and a simpler multi-tenant model, but Kafka has a larger ecosystem. It depends on your team’s familiarity.

Do I need a streaming platform if I only have batch jobs?

If your object storage events are processed in batches, you might not need a streaming platform. But if you need near-real-time processing, streaming helps.

How do I handle large objects (e.g., 1GB files) in streaming?

Streaming platforms work best with small messages. For large objects, consider sending only metadata in the event and having consumers fetch the object directly from storage.

What’s the difference between event streaming and message queues?

Event streaming platforms retain events for replay and support multiple consumers, while message queues typically delete messages after consumption. For object storage workloads, replay is often essential.

Can I use AWS Kinesis for S3 workloads?

Yes, Kinesis Data Streams can integrate with S3 via Lambda or Kinesis Firehose, but it has a 1MB record size limit and 24-hour retention by default.

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