The Kafka Ecosystem - Kafka Core, Streams, Connect, ksqlDB, and Schema Registry - 2025 Edition

🚀 What’s New in This 2025 Update

Major Updates and Changes

• Cloud-Native First - Fully integrated with Kubernetes and serverless
• AI/ML Integration - Direct pipelines to machine learning frameworks
• 200+ Connectors - Massive expansion of Kafka Connect ecosystem
• ksqlDB Maturity - Production-ready SQL streaming at scale
• Automated Operations - Self-healing, auto-scaling ecosystem
• Enhanced Security - Zero-trust architecture support

Deprecated Features

• ❌ Standalone REST Proxy - Replaced by API gateways
• ❌ Manual deployments - Cloud-native automation standard
• ❌ Legacy monitoring - Unified observability platforms
• ❌ Custom integration scripts - Managed connectors preferred

Ready to explore the complete Kafka ecosystem? Let’s discover how each component transforms streaming data into business value.

The Kafka Ecosystem - Modern Stream Processing Platform

mindmap
  root((Kafka Ecosystem 2025))
    Core Platform
      Kafka Brokers
      KRaft Controllers
      Topics & Partitions
      MirrorMaker 2.0
    Stream Processing
      Kafka Streams
      ksqlDB
      Flink Integration
      Spark Streaming
    Integration Layer
      Kafka Connect
      200+ Connectors
      CDC Sources
      Cloud Sinks
    Data Governance
      Schema Registry
      Data Contracts
      Evolution Rules
      Compatibility
    Cloud Native
      Kubernetes Operators
      Serverless Functions
      API Gateways
      Service Mesh

The Kafka ecosystem has evolved from a messaging platform into a complete event streaming platform. While Kafka Core handles the fundamental broker, topic, and partition management, the ecosystem provides everything needed for enterprise-grade streaming applications.

The modern ecosystem includes Kafka Streams for Java-based stream processing, ksqlDB for SQL-based streaming, Kafka Connect with hundreds of pre-built connectors, and Schema Registry for data governance. These components work together seamlessly, especially in cloud-native deployments.

Cloudurable provides Kafka training, Kafka consulting, Kafka support and helps setting up Kafka clusters in AWS.

Ecosystem Architecture Overview

flowchart TB
  subgraph Sources["Data Sources"]
    DB[(Databases)]
    API[REST APIs]
    IOT[IoT Devices]
    APPS[Applications]
    FILES[File Systems]
  end
  
  subgraph Connect["Kafka Connect"]
    SC[Source Connectors]
    SINK[Sink Connectors]
  end
  
  subgraph Core["Kafka Core"]
    TOPICS[Topics/Partitions]
    KRAFT[KRaft Controllers]
  end
  
  subgraph Processing["Stream Processing"]
    STREAMS[Kafka Streams]
    KSQL[ksqlDB]
    FLINK[Apache Flink]
  end
  
  subgraph Destinations["Data Destinations"]
    DW[(Data Warehouse)]
    LAKE[(Data Lake)]
    ML[ML Platforms]
    SEARCH[Search Engines]
    MONITOR[Monitoring]
  end
  
  Sources --> SC
  SC --> TOPICS
  TOPICS --> STREAMS
  TOPICS --> KSQL
  TOPICS --> FLINK
  STREAMS --> TOPICS
  KSQL --> TOPICS
  TOPICS --> SINK
  SINK --> Destinations
  
  classDef source fill:#bbdefb,stroke:#1976d2,stroke-width:1px,color:#333333
  classDef kafka fill:#e1bee7,stroke:#8e24aa,stroke-width:1px,color:#333333
  classDef process fill:#c8e6c9,stroke:#43a047,stroke-width:1px,color:#333333
  classDef dest fill:#fff9c4,stroke:#f9a825,stroke-width:1px,color:#333333
  
  class DB,API,IOT,APPS,FILES source
  class SC,SINK,TOPICS,KRAFT kafka
  class STREAMS,KSQL,FLINK process
  class DW,LAKE,ML,SEARCH,MONITOR dest

Kafka Streams - Stateful Stream Processing

Kafka Streams has matured into the go-to library for building sophisticated stream processing applications in Java. Key capabilities in 2025:

Core Features

• Exactly-once processing - Guaranteed with transactions
• Stateful operations - RocksDB-backed state stores
• Time-based operations - Event-time processing
• Interactive queries - Query state stores via REST
• Fault tolerance - Automatic state recovery

Modern Kafka Streams Architecture

classDiagram
  class StreamsApplication {
    +StreamsConfig config
    +Topology topology
    +KafkaStreams streams
    +start(): void
    +stop(): void
  }
  
  class Topology {
    +addSource(name, topics): void
    +addProcessor(name, supplier): void
    +addSink(name, topic): void
    +addStateStore(store): void
  }
  
  class KStream {
    +filter(predicate): KStream
    +map(mapper): KStream
    +groupByKey(): KGroupedStream
    +join(other): KStream
    +to(topic): void
  }
  
  class KTable {
    +filter(predicate): KTable
    +mapValues(mapper): KTable
    +join(other): KTable
    +toStream(): KStream
  }
  
  class StateStore {
    +name: String
    +get(key): Value
    +put(key, value): void
    +delete(key): void
    +range(from, to): Iterator
  }
  
  class InteractiveQueries {
    +getStore(name): ReadOnlyStore
    +getHostInfo(): HostInfo
    +queryMetadata(): StreamsMetadata
  }
  
  StreamsApplication "1" *-- "1" Topology
  StreamsApplication "1" *-- "1" KafkaStreams
  Topology "1" *-- "many" KStream
  Topology "1" *-- "many" KTable
  Topology "1" *-- "many" StateStore
  KafkaStreams "1" *-- "1" InteractiveQueries

Example: Real-Time Fraud Detection

// Modern Kafka Streams application for fraud detection
StreamsBuilder builder = new StreamsBuilder();

// Transaction stream
KStream<String, Transaction> transactions = 
    builder.stream("transactions");

// User profiles table (compacted topic)
KTable<String, UserProfile> profiles = 
    builder.table("user-profiles");

// Fraud detection logic
KStream<String, FraudAlert> fraudAlerts = transactions
    .join(profiles, 
        (transaction, profile) -> 
            new EnrichedTransaction(transaction, profile))
    .groupByKey()
    .windowedBy(TimeWindows.of(Duration.ofMinutes(5)))
    .aggregate(
        FraudScore::new,
        (key, transaction, score) -> 
            score.update(transaction),
        Materialized.with(Serdes.String(), fraudScoreSerde))
    .toStream()
    .filter((key, score) -> score.isSuspicious())
    .map((key, score) -> 
        KeyValue.pair(key.key(), new FraudAlert(score)));

// Output fraud alerts
fraudAlerts.to("fraud-alerts");

// Enable interactive queries
Properties props = new Properties();
props.put(StreamsConfig.APPLICATION_SERVER_CONFIG, "localhost:7070");

KafkaStreams streams = new KafkaStreams(builder.build(), props);
streams.start();

Cloud-Native Kafka Streams

flowchart TB
  subgraph K8s["Kubernetes Cluster"]
    subgraph Pod1["Streams Pod 1"]
      APP1[Streams App]
      STATE1[(State Store)]
      QUERY1[Query API]
    end
    
    subgraph Pod2["Streams Pod 2"]
      APP2[Streams App]
      STATE2[(State Store)]
      QUERY2[Query API]
    end
    
    subgraph Pod3["Streams Pod 3"]
      APP3[Streams App]
      STATE3[(State Store)]
      QUERY3[Query API]
    end
    
    LB[Load Balancer]
  end
  
  subgraph Kafka["Kafka Cluster"]
    T1[Input Topics]
    T2[Output Topics]
    CHANGELOG[Changelog Topics]
  end
  
  T1 --> APP1
  T1 --> APP2
  T1 --> APP3
  
  APP1 --> T2
  APP2 --> T2
  APP3 --> T2
  
  STATE1 <--> CHANGELOG
  STATE2 <--> CHANGELOG
  STATE3 <--> CHANGELOG
  
  LB --> QUERY1
  LB --> QUERY2
  LB --> QUERY3
  
  classDef k8s fill:#bbdefb,stroke:#1976d2,stroke-width:1px,color:#333333
  classDef kafka fill:#e1bee7,stroke:#8e24aa,stroke-width:1px,color:#333333
  
  class Pod1,Pod2,Pod3,LB k8s
  class T1,T2,CHANGELOG kafka

Kafka Connect - Universal Data Integration

Kafka Connect has exploded with 200+ production-ready connectors in 2025:

Popular Source Connectors

• Database CDC: Debezium for MySQL, PostgreSQL, MongoDB, Oracle
• Cloud Services: AWS S3, Azure Blob, Google Cloud Storage
• SaaS Applications: Salesforce, ServiceNow, Zendesk
• IoT Platforms: MQTT, CoAP, Industrial protocols
• Message Queues: RabbitMQ, ActiveMQ, IBM MQ

Popular Sink Connectors

• Data Warehouses: Snowflake, BigQuery, Redshift
• Search Engines: Elasticsearch, OpenSearch, Solr
• Databases: JDBC, MongoDB, Cassandra
• Object Storage: S3, MinIO, Azure Blob
• Observability: Datadog, Splunk, New Relic

Connect Architecture

flowchart TB
  subgraph ConnectCluster["Connect Cluster (Distributed Mode)"]
    W1[Worker 1<br>Tasks: 1-3]
    W2[Worker 2<br>Tasks: 4-6]
    W3[Worker 3<br>Tasks: 7-9]
    
    CONFIG[Config Topic]
    OFFSET[Offset Topic]
    STATUS[Status Topic]
  end
  
  subgraph Connectors["Active Connectors"]
    C1[MySQL CDC<br>Source]
    C2[S3 Sink]
    C3[Elasticsearch<br>Sink]
  end
  
  subgraph Management["Management"]
    API[REST API]
    UI[Connect UI]
    OPERATOR[K8s Operator]
  end
  
  C1 -.-> W1
  C2 -.-> W2
  C3 -.-> W3
  
  W1 <--> CONFIG
  W2 <--> OFFSET
  W3 <--> STATUS
  
  API --> W1
  UI --> API
  OPERATOR --> W1
  
  classDef worker fill:#c8e6c9,stroke:#43a047,stroke-width:1px,color:#333333
  classDef connector fill:#bbdefb,stroke:#1976d2,stroke-width:1px,color:#333333
  classDef mgmt fill:#fff9c4,stroke:#f9a825,stroke-width:1px,color:#333333
  
  class W1,W2,W3 worker
  class C1,C2,C3 connector
  class API,UI,OPERATOR mgmt

Example: Real-Time Data Pipeline

// Debezium MySQL Source Connector
{
  "name": "mysql-source",
  "config": {
    "connector.class": "io.debezium.connector.mysql.MySqlConnector",
    "database.hostname": "mysql.example.com",
    "database.port": "3306",
    "database.user": "debezium",
    "database.password": "${secrets:mysql/password}",
    "database.server.id": "184054",
    "database.server.name": "fullfillment",
    "database.include.list": "inventory",
    "transforms": "route,unwrap",
    "transforms.route.type": "org.apache.kafka.connect.transforms.RegexRouter",
    "transforms.route.regex": "([^.]+)\\.([^.]+)\\.([^.]+)",
    "transforms.route.replacement": "$3",
    "transforms.unwrap.type": "io.debezium.transforms.ExtractNewRecordState"
  }
}

// Snowflake Sink Connector
{
  "name": "snowflake-sink",
  "config": {
    "connector.class": "com.snowflake.kafka.connector.SnowflakeSinkConnector",
    "topics": "orders,customers,products",
    "snowflake.url.name": "account.snowflakecomputing.com",
    "snowflake.user.name": "kafka_connector",
    "snowflake.private.key": "${secrets:snowflake/private_key}",
    "snowflake.database.name": "raw_data",
    "snowflake.schema.name": "kafka",
    "buffer.count.records": "10000",
    "buffer.flush.time": "60"
  }
}

ksqlDB - SQL for Streaming

ksqlDB brings SQL to streaming, making stream processing accessible to analysts and data engineers:

Key Features in 2025

• Materialized views - Continuously updated tables
• Stream-table joins - Enrich streams with reference data
• Windowed aggregations - Time-based analytics
• User-defined functions - Custom processing logic
• Pull queries - Request/response on state
• Push queries - Real-time subscriptions

ksqlDB Architecture

classDiagram
  class ksqlDB {
    +engine: StreamsEngine
    +metastore: Metastore
    +restServer: RestServer
    +executeQuery(sql): Result
  }
  
  class Stream {
    +name: String
    +schema: Schema
    +topic: String
    +keyFormat: Format
    +valueFormat: Format
  }
  
  class Table {
    +name: String
    +schema: Schema
    +topic: String
    +isWindowed: boolean
    +stateStore: String
  }
  
  class PersistentQuery {
    +id: String
    +statement: String
    +topology: Topology
    +state: QueryState
  }
  
  class PullQuery {
    +statement: String
    +target: Table
    +execute(): Rows
  }
  
  class PushQuery {
    +statement: String
    +target: Stream/Table
    +subscribe(): Publisher
  }
  
  ksqlDB "1" *-- "many" Stream
  ksqlDB "1" *-- "many" Table
  ksqlDB "1" *-- "many" PersistentQuery
  ksqlDB "1" --> "many" PullQuery
  ksqlDB "1" --> "many" PushQuery

Example: Real-Time Analytics

-- Create stream from Kafka topic
CREATE STREAM pageviews (
  user_id VARCHAR,
  page_id VARCHAR,
  duration_ms INT,
  ts TIMESTAMP
) WITH (
  KAFKA_TOPIC='pageviews',
  VALUE_FORMAT='JSON',
  TIMESTAMP='ts'
);

-- Create user profile table
CREATE TABLE users (
  user_id VARCHAR PRIMARY KEY,
  username VARCHAR,
  region VARCHAR,
  level VARCHAR
) WITH (
  KAFKA_TOPIC='users',
  VALUE_FORMAT='AVRO'
);

-- Real-time user activity enrichment
CREATE STREAM enriched_activity AS
  SELECT 
    p.user_id,
    u.username,
    u.region,
    u.level,
    p.page_id,
    p.duration_ms,
    p.ts
  FROM pageviews p
  INNER JOIN users u ON p.user_id = u.user_id
  EMIT CHANGES;

-- Windowed aggregation for metrics
CREATE TABLE user_activity_stats AS
  SELECT
    user_id,
    COUNT(*) as page_count,
    SUM(duration_ms) as total_duration,
    AVG(duration_ms) as avg_duration
  FROM pageviews
  WINDOW TUMBLING (SIZE 5 MINUTES)
  GROUP BY user_id
  EMIT CHANGES;

-- Anomaly detection
CREATE TABLE suspicious_users AS
  SELECT
    user_id,
    COUNT(*) as request_count
  FROM pageviews
  WINDOW HOPPING (SIZE 1 MINUTE, ADVANCE BY 10 SECONDS)
  GROUP BY user_id
  HAVING COUNT(*) > 1000
  EMIT CHANGES;

Schema Registry - Data Governance

Schema Registry ensures data compatibility and enables schema evolution:

Modern Features

• Multi-format support - Avro, JSON Schema, Protobuf
• Schema evolution rules - Forward, backward, full compatibility
• Schema references - Compose complex schemas
• Data contracts - Enforce at producer/consumer
• Security - ACLs and encryption

Schema Registry Architecture

flowchart TB
  subgraph Producers["Producers"]
    P1[Service A]
    P2[Service B]
  end
  
  subgraph Registry["Schema Registry"]
    SCHEMAS[(Schema Store)]
    COMPAT[Compatibility Checker]
    CACHE[Schema Cache]
    API[REST API]
  end
  
  subgraph Consumers["Consumers"]
    C1[Analytics]
    C2[ML Pipeline]
  end
  
  P1 -->|Register Schema| API
  P2 -->|Check Compatibility| COMPAT
  API --> SCHEMAS
  COMPAT --> SCHEMAS
  SCHEMAS --> CACHE
  
  C1 -->|Fetch Schema| CACHE
  C2 -->|Fetch Schema| CACHE
  
  classDef producer fill:#bbdefb,stroke:#1976d2,stroke-width:1px,color:#333333
  classDef registry fill:#e1bee7,stroke:#8e24aa,stroke-width:1px,color:#333333
  classDef consumer fill:#c8e6c9,stroke:#43a047,stroke-width:1px,color:#333333
  
  class P1,P2 producer
  class SCHEMAS,COMPAT,CACHE,API registry
  class C1,C2 consumer

Example: Schema Evolution

// Version 1 - Initial schema
{
  "type": "record",
  "name": "User",
  "namespace": "com.example",
  "fields": [
    {"name": "id", "type": "string"},
    {"name": "name", "type": "string"},
    {"name": "email", "type": "string"}
  ]
}

// Version 2 - Add optional field (backward compatible)
{
  "type": "record",
  "name": "User",
  "namespace": "com.example",
  "fields": [
    {"name": "id", "type": "string"},
    {"name": "name", "type": "string"},
    {"name": "email", "type": "string"},
    {"name": "age", "type": ["null", "int"], "default": null}
  ]
}

// Version 3 - Add required field with default (backward compatible)
{
  "type": "record",
  "name": "User",
  "namespace": "com.example",
  "fields": [
    {"name": "id", "type": "string"},
    {"name": "name", "type": "string"},
    {"name": "email", "type": "string"},
    {"name": "age", "type": ["null", "int"], "default": null},
    {"name": "created_at", "type": "long", "default": 0}
  ]
}

Cloud-Native Integration

Kubernetes Operator Pattern

apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: production-cluster
spec:
  kafka:
    version: 3.6.0
    replicas: 6
    listeners:
      - name: plain
        port: 9092
        type: internal
      - name: tls
        port: 9093
        type: internal
        tls: true
    config:
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 1Ti
        class: fast-ssd
  entityOperator:
    topicOperator: {}
    userOperator: {}

Serverless Integration

flowchart LR
  subgraph Events["Event Sources"]
    E1[API Gateway]
    E2[S3 Events]
    E3[DynamoDB Streams]
  end
  
  subgraph Lambda["Lambda Functions"]
    L1[Event Router]
    L2[Transformer]
    L3[Enricher]
  end
  
  subgraph Kafka["Kafka"]
    T1[Raw Events]
    T2[Processed Events]
  end
  
  subgraph Consumers["Event Consumers"]
    C1[Analytics Lambda]
    C2[ML Pipeline]
    C3[Data Lake]
  end
  
  E1 --> L1
  E2 --> L1
  E3 --> L1
  L1 --> T1
  T1 --> L2
  L2 --> L3
  L3 --> T2
  T2 --> C1
  T2 --> C2
  T2 --> C3
  
  classDef event fill:#bbdefb,stroke:#1976d2,stroke-width:1px,color:#333333
  classDef lambda fill:#c8e6c9,stroke:#43a047,stroke-width:1px,color:#333333
  classDef kafka fill:#e1bee7,stroke:#8e24aa,stroke-width:1px,color:#333333
  
  class E1,E2,E3 event
  class L1,L2,L3,C1 lambda
  class T1,T2 kafka

MirrorMaker 2.0 - Cross-Cluster Replication

MirrorMaker 2.0 provides sophisticated multi-cluster replication:

flowchart TB
  subgraph DC1["Data Center 1"]
    K1[Kafka Cluster 1]
    MM1[MirrorMaker 2.0]
  end
  
  subgraph DC2["Data Center 2"]
    K2[Kafka Cluster 2]
    MM2[MirrorMaker 2.0]
  end
  
  subgraph Cloud["Cloud Region"]
    K3[Kafka Cluster 3]
    MM3[MirrorMaker 2.0]
  end
  
  K1 <-->|Active-Active| K2
  K1 -->|Active-Passive| K3
  K2 -->|Active-Passive| K3
  
  MM1 -.-> K2
  MM1 -.-> K3
  MM2 -.-> K1
  MM2 -.-> K3
  MM3 -.-> K1
  MM3 -.-> K2
  
  classDef dc fill:#bbdefb,stroke:#1976d2,stroke-width:1px,color:#333333
  classDef cloud fill:#e1bee7,stroke:#8e24aa,stroke-width:1px,color:#333333
  
  class K1,MM1,K2,MM2 dc
  class K3,MM3 cloud

Ecosystem Best Practices

1. Design for Evolution

• Use Schema Registry from day one
• Plan for backward compatibility
• Version your data contracts
• Document schema changes

2. Embrace Managed Services

• Reduce operational overhead
• Automatic scaling and updates
• Built-in monitoring and security
• Focus on business logic

3. Choose the Right Tool

• Kafka Streams: Complex stateful processing in Java
• ksqlDB: SQL-based processing and analytics
• Kafka Connect: Data integration without code
• External: Flink/Spark for advanced analytics

4. Monitor Everything

• Stream processing lag
• Connector health and throughput
• Schema compatibility violations
• Cross-region replication lag

5. Security First

• Enable encryption everywhere
• Use ACLs and RBAC
• Implement data masking
• Audit all access

Ecosystem Review

What is Kafka Streams best for?

Building sophisticated, stateful stream processing applications in Java with exactly-once guarantees, interactive queries, and fault tolerance.

When should I use Kafka Connect?

For integrating external systems without writing code. With 200+ connectors, it handles most data integration needs out of the box.

What makes ksqlDB powerful?

SQL interface for streaming data, making real-time analytics accessible to analysts. Perfect for materialized views and continuous queries.

Why is Schema Registry critical?

Ensures data compatibility across producers and consumers, enables safe schema evolution, and provides data governance.

How does MirrorMaker 2.0 improve on v1?

Automatic topic discovery, preserves partitions and offsets, supports active-active replication, and integrates with Kafka Connect.

What’s the future of the ecosystem?

Deeper cloud-native integration, AI/ML pipelines, automated operations, and unified management platforms.

Related Content

• What is Kafka?
• Kafka Architecture
• Kafka Topic Architecture
• Kafka Consumer Architecture
• Kafka Producer Architecture
• Kafka Low Level Design
• Kafka Log Compaction
• Kafka and Schema Registry
• Kafka Ecosystem
• Kafka vs. JMS
• Kafka versus Kinesis
• Kafka Command Line Tutorial
• Kafka Failover Tutorial

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