Laminar vs Spark Streaming

True streaming vs micro-batch processing. Achieve sub-second latency with a fraction of the resources.

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Key Differences

True Streaming

Event-at-a-time processing. Spark uses micro-batches with inherent latency.

Low Memory

Rust-based with minimal footprint. No JVM overhead or garbage collection pauses.

Instant Start

No cluster warm-up time. Spark requires driver/executor initialization.

Native Iceberg

Direct write with compaction. Better than Spark's Iceberg sink connector.

Performance Comparison

End-to-End Latency

Time from event to queryable

Spark Structured Streaming60.0Kms
Laminar1.0Kms

Lower is better

Memory per Worker

JVM vs Rust efficiency

Spark Executor8.0 GB
Laminar Worker2.0 GB

Lower is better

Startup Time

Time to process first event

Spark on YARN60.0s
Spark on K8s30.0s
Laminar2.0s

Lower is better

Monthly Cost

10M events/day workload

EMR + Spark3.0K$
Databricks4.5K$
Laminar800.0$

Lower is better

No comparison data

When to Use Each

Choose Spark Streaming if:

  • You already have a Spark infrastructure
  • Need complex ML/analytics with Spark MLlib
  • Minutes of latency is acceptable
  • Need unified batch and streaming

Choose Laminar if:

  • Sub-second latency is required
  • Want lower infrastructure costs
  • Primary use case is data ingestion
  • Prefer SQL-first development
  • Need fast startup and scale-down

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