Streaming Platform Comparison

Executive Summary

All three platforms deliver production‑grade streaming. Your best choice depends on team skills, ecosystem integrations, and operational constraints:

• Kafka: broadest ecosystem and managed options; great default for enterprises with existing Kafka skills/tooling.
• Pulsar: separates compute and storage via BookKeeper; strong for multi‑tenancy, geo‑replication, and tiered storage by design.
• Redpanda: Kafka‑API compatible with a single‑binary architecture; known for low operational overhead and high performance on modern hardware.

Feature Comparison (at a glance)

Notes: precise capabilities vary by version and distribution (open‑source vs managed/enterprise). Validate against your provider’s current documentation.

Performance Expectations

Throughput and latency are primarily driven by topic/partition design, message size, acks, batching, network, and storage configuration. In our field work, all three can meet real‑time ML requirements (p50 < 10–20 ms, p99 < 100–200 ms) with proper tuning and hardware.

• Kafka: predictable performance with mature guidance on partitioning and ISR sizing.
• Pulsar: benefits from BookKeeper ledger placement and offload for long retention.
• Redpanda: strong single‑host latency; shines with modern NVMe and many cores.

Operational Considerations

• Schema & governance: use a registry (Confluent, Karapace, Apicurio) regardless of platform.
• Observability: end‑to‑end tracing and consumer lag metrics are critical for ML freshness SLAs.
• Disaster recovery: plan for cross‑cluster replication and automated failover testing.
• Cost: evaluate tiered storage vs hot retention; right‑size partitions to avoid small‑files overhead.

Recommendations by Use Case

Next Steps

See our reference design for integrating streaming into ML systems and feature stores:

• Streaming Data Pipeline for ML
• Real‑time Feature Store Architecture