Kafka Stream Processing Pipeline (Producer → Topics → Consumers → Sinks)

Data Pipelines · flowchart diagram · Apache-2.0

End-to-end Kafka pipeline showing app events, CDC, and IoT telemetry flowing through topics, stream processors (Kafka Streams, ksqlDB, Flink), and sinking into Snowflake, Elasticsearch, and S3.

Source: https://kafka.apache.org/documentation/streams/
Curated by Archigram editorial
kafka streaming etl cdc flink ksqldb

Mermaid source

flowchart LR
    subgraph Sources
        APP[App Events]
        DB[CDC from Postgres]
        IOT[IoT Telemetry]
    end

    subgraph Producers
        P1[Event Producer]
        P2[Debezium]
        P3[MQTT Bridge]
    end

    subgraph Kafka[Apache Kafka Cluster]
        T1[(events.user)]
        T2[(cdc.orders)]
        T3[(telemetry.raw)]
        T4[(events.enriched)]
    end

    subgraph Processing
        SP1[Kafka Streams: Enrich]
        SP2[ksqlDB: Aggregate]
        SP3[Flink Job: Sessionize]
    end

    subgraph Sinks
        WH[(Snowflake)]
        ES[(Elasticsearch)]
        S3[(S3 Data Lake)]
        ALERT[Alerting Service]
    end

    APP --> P1 --> T1
    DB --> P2 --> T2
    IOT --> P3 --> T3

    T1 --> SP1
    T2 --> SP1
    SP1 --> T4
    T4 --> SP2
    T3 --> SP3

    SP2 --> WH
    SP2 --> ES
    SP3 --> S3
    SP3 --> ALERT

What this diagram shows

A typical event-driven data platform built on Kafka. Three source families produce into Kafka topics: application events through a custom producer, change-data-capture from Postgres via Debezium, and IoT telemetry through an MQTT bridge. Stream processors then enrich, aggregate, and sessionize; a Kafka Streams job joins user events with CDC data into an enriched topic, ksqlDB rolls aggregates for the warehouse and search index, and a Flink job handles sessionization for IoT before writing to the lake and triggering alerts.

When to use it

Pick this shape when you need a single source of truth for events that fans out to multiple consumers — analytics, search, alerting, and downstream services — without each producer having to know about every consumer. It is the right backbone when more than one team needs to subscribe to the same firehose, when you want exactly-once semantics for downstream sinks, and when you anticipate adding new consumers over time without changing the producers.

How to adapt it for your project

If you do not have an on-prem Kafka, swap in Confluent Cloud, AWS MSK, or Redpanda — the topology stays the same. For lower operational overhead, replace the dedicated Flink cluster with Kafka Streams everywhere (good up to mid-volume; Flink wins for high cardinality state and complex windowing). If you are starting small, collapse the three source families into one topic and one consumer group; you can always split later. For ML feature pipelines, add a Feature Store sink alongside Snowflake.

Key concepts

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