With ETL, ELT, Spark, Flink, and Kafka Streams all on the table, how do you choose? The answer comes down to three questions:
1. What is your latency requirement?
| Latency Needed | Approach |
|---|---|
| Hours to days | Batch (Spark) |
| Minutes | Micro-batch (Spark Structured Streaming) |
| Milliseconds | True streaming (Flink) |
2. What is the complexity of your processing logic?
| Complexity | Approach |
|---|---|
| Simple filtering, aggregation, joins | Kafka Streams or SQL in your warehouse |
| Complex stateful logic, pattern detection | Apache Flink |
| Large-scale historical analysis, ML training | Apache Spark |
3. What is your operational capacity?
| Team Capacity | Approach |
|---|---|
| Small team, no dedicated data infra | Managed services (AWS Kinesis, Google Dataflow, Confluent Cloud) |
| Medium team, Kafka already in use | Kafka Streams |
| Dedicated data engineering team | Flink or Spark on Kubernetes |
The Lambda Architecture combines batch and streaming layers to get the best of both worlds: a Speed Layer (Flink) for real-time approximate results, and a Batch Layer (Spark) for accurate historical results. The Serving Layer merges both.
The Kappa Architecture is a simplification: use only a streaming layer (Flink), but make it capable enough to reprocess historical data by replaying Kafka topics. This reduces operational complexity at the cost of requiring a more powerful streaming system.