Apache Kafka

Produce transaction events to Kafka.

Process streams using Kafka Streams or Flink.

Compare against fraud models.

Trigger alert or block if fraudulent.

Ingest user behavior logs.

Transform logs into features in real-time.

Sink to vector database or Redis via Kafka Connect.

Serve to AI model.

Services emit events to Kafka topics.

Downstream services subscribe to relevant topics.

State is maintained asynchronously.

System remains resilient if one service fails.

Agents collect logs and produce to Kafka.

Kafka acts as a massive buffer.

Data is consumed by Elasticsearch or Splunk for indexing.

Avoids overwhelming the indexing engine during traffic spikes.

Sensors push data to MQTT bridge.

Bridge produces to Kafka.

Kafka Streams filters 'noise' data.

Aggregated data is stored in a time-series database.

Point-of-sale systems produce purchase events.

Kafka replicates events across regions.

Inventory services consume events to update local caches.

Prevents overselling during high-traffic events.

Debezium monitors database transaction logs.

Changes are streamed into Kafka topics.

Data is transformed into the target schema.

Kafka Connect sinks data into a data warehouse like BigQuery.