01The three players in plain English
A distributed log. Kafka is the transport: it stores ordered streams of events durably and lets many consumers read them. It is not, by itself, a stream processor.
Data & Cloud · August 31, 2025
Real-Time Data Streaming: Kafka vs Flink vs Spark Streaming Compared
A clear comparison of the three big real-time data platforms, with the use cases each one wins and a reference architecture you can adapt.
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Binate AI
August 31, 2025
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02Pick by use case
Use Flink when…
You need true low latency
- Fraud detection on transactions
- Real-time pricing or routing
- Complex event processing with windows and joins
- You need exactly-once with side effects
Use Spark Streaming when…
You already run Spark
- You have Spark expertise on the team
- Sub-second is good enough
- You want the same code to run on batch and stream
- You are integrating with Delta Lake
03A reference architecture
- 1
Sources publish to Kafka topics
Application services, change data capture from databases, and external webhooks all land in Kafka.
- 2
Schema registry enforces contracts
Use Avro or Protobuf with a schema registry so producers and consumers cannot drift apart.
- 3
Flink (or Spark) processes streams
Aggregations, joins, enrichment, ML inference all happen here.
- 4
Sinks deliver results
Materialized views for analytics, alerting systems, transactional databases for downstream services.
- 5
A separate lake holds raw events
Kafka retains for days, the lake (S3/GCS) keeps everything for reprocessing.
04A working Flink job
Flink Python API
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.common import Time
env = StreamExecutionEnvironment.get_execution_environment()
events = env.add_source(KafkaSource(topic="transactions"))
def windowed_count(stream):
return (stream
.key_by(lambda e: e.account_id)
.window(TumblingEventTimeWindows.of(Time.minutes(1)))
.reduce(lambda a, b: Count(a.amount + b.amount)))
windowed_count(events).add_sink(KafkaSink(topic="account_per_minute"))
env.execute("per-account-1m")05The mistakes that bite
06Cost and latency budgets
50ms
Flink P95 event-to-output
500ms
Spark micro-batch P95
$1.2k/mo
Median Kafka cluster (managed)
07
Need real-time data pipelines in production?
We design Kafka + Flink architectures for fraud, fintech, and logistics workloads.
See our streaming workThe takeaway
Kafka is your transport, Flink is your processor when latency is the product, Spark is your processor when familiarity is. Pick by latency and team skill, not hype.
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