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Streaming vs Batch: When Does It Matter?
| Dimension | Batch | Streaming |
|---|---|---|
| Latency | Minutes to Hours | Milliseconds to Seconds |
| Use Case | Reports, warehouse loads, ML training | Fraud, live dashboards, IoT |
| Complexity | Lower | Higher |
| Cost | Pay for compute bursts | Always-on infrastructure |
| Tools | Airflow, dbt, Spark | Kafka, Flink, Kinesis |
Rule of Thumb
If your stakeholders check a dashboard once a day, streaming is overkill. If they expect data within seconds, batch won't cut it. Default to batch, add streaming where latency requirements demand it.
Apache Kafka: The Foundation
Kafka is the distributed event streaming backbone for most streaming architectures. It decouples producers (apps writing events) from consumers (services processing events).
Topics
Named streams of events. Each event has a key, value, timestamp, and optional headers.
Partitions
Topics split into partitions for parallelism. Events with the same key go to the same partition (ordering guarantee).
Consumer Groups
Multiple consumers share partitions for horizontal scaling. Each partition is consumed by exactly one consumer in a group.
Retention
Events are retained for a configurable period (default 7 days). Can be set to infinite for event sourcing patterns.
Stream Processing with Flink
Apache Flink processes events as they arrive with exactly-once semantics. It handles windowing, state management, and complex event processing that Kafka Streams can't do at scale.
Windowed Aggregations
Tumbling, sliding, and session windows. Example: count events per 5-minute window, or aggregate user sessions until 30 min idle.
Stateful Processing
Flink maintains state across events. Join streams, detect patterns, and compute running totals without external databases.
Exactly-Once Semantics
With checkpointing, Flink guarantees each event is processed exactly once, even during failures. Critical for financial data.
SQL Interface
Flink SQL lets analysts write streaming queries in familiar SQL syntax. No need to learn Java/Scala APIs for basic transformations.
Architecture Patterns
Lambda Architecture
Parallel batch + streaming layers. Batch provides accuracy, streaming provides speed. Results merge in a serving layer.
Pros: Accuracy + speed. Cons: Two codebases, complex maintenance.
Kappa Architecture
Streaming-only. Single codebase processes all events. Reprocessing by replaying from Kafka. Simpler than Lambda.
Pros: One codebase, simpler. Cons: Harder to handle heavy aggregations, replay can be slow.
Hybrid (Recommended)
Stream low-latency use cases (fraud, alerts). Batch the warehouse and complex aggregations. Share the same Kafka backbone.
Pros: Right tool for each job. Cons: Two systems to manage.
Platform Comparison
| Platform | Best For | Managed? |
|---|---|---|
| Apache Kafka | Event backbone, high throughput | Confluent Cloud / MSK |
| Amazon Kinesis | AWS-native streaming | Fully managed |
| Google Pub/Sub | GCP-native, serverless | Fully managed |
| Redpanda | Kafka-compatible, no JVM | Redpanda Cloud |
| Apache Flink | Complex stream processing | AWS KDA / Ververica |
FAQ
When should you use streaming vs batch?
What is Apache Kafka?
What are Kafka alternatives?
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