Kapan sampeyan kudu nggunakake Kafka (lan kapan ora)?

Question

Accepted Answer

Kafka kuat nanging **ora selamane peralatan sing tepat** — luwih apik kanggo high-volume event streaming, pipeline, lan sistem event-driven, nanging nambah kerumitan operasional sing ditindakake dening peralatan sing luwih sederhana. Ngerti kapan Kafka pas (lan kapan mubazir) nglambangake pertimbangan sing sehat.

## Kapan Kafka cocog

```text
✓ HIGH-VOLUME event streaming / data → millions of events; high throughput needs
✓ DATA PIPELINES → streaming data reliably between many systems (a data backbone)
✓ MULTIPLE CONSUMERS of the same stream → many independent consumers/groups read the data
✓ EVENT-DRIVEN architecture / event sourcing → events as a durable record
✓ REPLAY needed → re-read historical events
✓ REAL-TIME stream processing / analytics
→ Kafka shines for scale, streaming, retention, and multiple consumers
```

## Kapan Kafka bisa uga mubazir

```text
⚠️ Kafka adds OPERATIONAL COMPLEXITY (a distributed cluster to run, tune, monitor):
  → SIMPLE messaging / task queues → a simpler queue (RabbitMQ, SQS, Redis) may suffice
  → LOW volume → Kafka's scale is unneeded; simpler tools are easier
  → request/REPLY, complex routing → traditional queues fit better
  → just need a basic background job queue → don't reach for Kafka
→ don't add Kafka's complexity if you don't need its capabilities (YAGNI)
```

## Milih

```text
✓ Use Kafka when you NEED its strengths (scale, streaming, retention, replay, many consumers)
✓ Use simpler tools (RabbitMQ, SQS, Redis, a database) when those suffice
✓ Consider MANAGED Kafka (Confluent, MSK) to reduce operational burden if you do use it
→ match the tool to the actual requirements, not hype
```

## Kenapa iki penting

Ngerti kapan nggunakake Kafka (lan kapan ora) penting amarga **Kafka kuat nanging nambah kerumitan**, mula pertimbangan kanggo nggunakake kanthi pas penting kanggo keputusan arsitektur sing sehat.

Kafka luwih apik ing kasusu tartamtu nanging ora solusi universal, lan ngerti kapan pas versus kapan mubazir nglambangake pertimbangan sing dewasa.

Ngerti **kapan Kafka cocog** — high-volume event streaming, data pipeline (tulang punggung data), akses saka akeh consumer menyang aliran sing padha, arsitektur event-driven lan event sourcing, kabutuhan replay, lan real-time stream processing — klarifikasi ing mana kekuatan Kafka (skala, streaming, retention, akeh consumer) ndadekake pilihan sing tepat.

Sakabehe, ngerti **kapan Kafka bisa uga mubazir** padha pentinge: Kafka nambah **kerumitan operasional** (kluster terdistribusi sing kudu ditalakake, dituning, lan dimonitor), mula kanggo messaging sederhana utawa task queue, volume kurang, request/reply, utawa basic background job queue, **peralatan sing luwih sederhana (RabbitMQ, SQS, Redis) asring cukup lan luwih gampang** — lan nambahake kerumitan Kafka nalika sampeyan ora butuh kapabilitas kasebut minangka over-engineering (YAGNI).

Perspektif seimbang iki — ngenali kekuatan Kafka nanging uga biaya lan manawa peralatan sing luwih sederhana asring cocog — penting amarga Kafka kadhang diadopsi amarga hyp bukananalisis nyata.

Ngerti carane **milih** — nggunakake Kafka nalika sampeyan butuh kekuatan kasebut (skala, streaming, retention, replay, akeh consumer), nggunakake peralatan luwih sederhana nalika cukup, lan nimbang Kafka terkelola kanggo ngurangi beban operasional yen sampeyan pancene nggunakaake — nglambangake ncocokake peralatan menyang kabutuhan nyata bukananalisis hyp.

Amarga Kafka kuat nanging nambah kerumitan operasional (mula ora selamane peralatan sing tepat) lan ngerti kapan pas (kekuatane) versus kapan peralatan sing luwih sederhana cukup (nglindari over-engineering) nglambangake pertimbangan sing sehat, lan amarga iki informasi keputusan arsitektur sing apik, ngerti kapan nggunakake Kafka minangka wawasan sing penting lan praktis — penting kanggo pertimbangan nggunakake Kafka kanthi pas (nalika kekuatane dibutuhake) versus peralatan luwih sederhana (nalika cukup), nglindari over-engineering ngadopsi kerumitan Kafka sing ora perlu, lan nglambangake pengambilan keputusan arsitektur sing sehat lan tahan hyp.