Apa saé kagunan umum kanggo Kafka?

Question

Accepted Answer

Kafka digunakake ing akèh skenario sing melibatake **data volume tinggi, real-time, utawa streaming** — messaging, data pipelines, event-driven architectures, stream processing, log aggregation, lan sapanunggalé. Ngerteni kagunan kasebut klarifikasiné dadi ngerti Kafka cocog ngendi.

## Kagunan umum

```text
✓ MESSAGING / event streaming → decoupled pub/sub between services at scale
✓ DATA PIPELINES / integration → reliably stream data between systems (databases, services,
  data warehouses, analytics) — a central data "backbone"
✓ EVENT-DRIVEN ARCHITECTURE → services emit and react to events; event sourcing (events as
  the source of truth)
✓ STREAM PROCESSING → real-time processing/analytics on event streams (Kafka Streams, Flink)
✓ LOG AGGREGATION → collect logs/metrics from many services into one stream
✓ ACTIVITY TRACKING → user activity, clickstreams, telemetry at high volume
✓ CHANGE DATA CAPTURE (CDC) → stream database changes to other systems
✓ METRICS / monitoring → real-time metrics collection and processing
```

## Alasan Kafka cocog kanggo kasebut

```text
→ HIGH THROUGHPUT → handles massive event volumes (activity, metrics, CDC)
→ DURABLE + RETAINED → reliable pipelines; replay; multiple consumers of the same data
→ DECOUPLING → producers and consumers independent (event-driven, integration)
→ REAL-TIME → low-latency streaming for real-time processing/analytics
→ SCALABLE → grows with data volume
→ a central "nervous system" for data in many architectures
```

## Ngapa iku penting

Ngerteni kagunan umum Kafka iku berharga amarga **klarifikasiné dadi ngerti Kafka cocog lan kapan kudu nggunakaké**, dadi pengetahuan sing berguna kanggo kaputusan arsitektur.

Kafka unggul ing skenario sing melibatake data volume tinggi, real-time, utawa streaming, lan ngerteni kagunan spesifik iku tulung ngakui nalika Kafka minangka tool sing bener.

**Kagunan umum** — **messaging/event streaming** (decoupled pub/sub ing skala gedhe), **data pipelines/integration** (streaming data antara sistem kanthi andal minangka central data backbone — kagunan Kafka sing utama), **event-driven architecture lan event sourcing** (layanan sing ngucapake lan mréspon event), **stream processing** (analytics real-time ing event streams), **log aggregation**, **activity tracking** (clickstreams, telemetry), **change data capture** (streaming database changes), lan **metrics/monitoring** — ngilustrasikaké aplikabilitas Kafka sing luwes kanggo skenario data-intensive, real-time.

Ngerteni **alasan Kafka cocog kanggo kasebut** — throughput tinggi (nangani volume event gedhe), durability lan retention (reliable pipelines, replay, multiple consumers saka data sing sama), decoupling (producers lan consumers independen), streaming real-time low-latency, lan scalability — nyambungake karakteristik Kafka menyang kagunan, klarifikasiné alasan kok dipilih.

Framing Kafka minangka **"nervous system" pusat kanggo data** ing akèh arsitektur nagkepake perane minangka data backbone.

Ngerteni kagunan iki tulung ngakui nalika Kafka cocog (streaming volume tinggi, pipelines, event-driven systems, real-time processing) bali nalika tools sing luwih sederhana cukup.

Samesthi Kafka unggul ing skenario spesifik (volume tinggi, real-time, streaming data) lan ngerteni kagunan umum (messaging, pipelines, event-driven, stream processing, CDC, lsp.) klarifikasiné dadi ngerti cocog lan kapan kudu nggunakaké, lan amarga iku ngaruhi kaputusan arsitektur, ngerteni kagunan umum Kafka iku pengetahuan berharga, praksis-relevan — klarifikasiné cocog dadi (volume tinggi, real-time, streaming, pipelines, event-driven systems), tulung ngakui nalika iku tool sing bener, lan berguna kanggo kaputusan arsitektur sing melibatake data streaming lan event-driven systems.