Showcase Your Apache Kafka Skills with a GitHub README Badge

Use this markdown in your README:

![Apache Kafka](https://img.shields.io/badge/Apache%20Kafka-231F20?style=for-the-badge&logo=apachekafka&logoColor=white)

The #231F20 is the near-black color from the Apache Kafka brand palette. The apachekafka logo identifier renders the Apache Kafka logo from Simple Icons. This dark badge is distinctive in a badge row — it signals low-level infrastructure work rather than application-layer tooling and stands out clearly against lighter-colored framework badges.

Kafka is a deep system with many facets. Specify which aspects you have production experience with:

• Producer/Consumer API: Publishing events, consumer group management, offset management
• Topics and partitions: Partition design for parallelism, replication factor for fault tolerance
• Kafka Streams: Stateful stream processing with join operations and windowing
• Kafka Connect: Source and sink connectors for database CDC (Change Data Capture)
• Schema Registry: Avro/Protobuf schemas with backward/forward compatibility guarantees
• Operations: Broker configuration, topic retention policies, consumer lag monitoring

Producer/consumer basics are table stakes — mentioning Kafka Streams operations or Schema Registry with compatibility guarantees signals substantially deeper Kafka expertise that hiring managers at data-intensive companies specifically look for.

Kafka appears at the center of distributed data architectures:

• Data pipeline stack: Kafka + Python + Spark + Airflow + PostgreSQL
• Microservices event bus: Kafka + Docker + Kubernetes + Node.js/Java
• Real-time analytics: Kafka + Flink/Spark Streaming + Elasticsearch + Grafana
• CDC pipeline: Kafka + Debezium + PostgreSQL + Elasticsearch

Kafka and traditional message queues (RabbitMQ, SQS) solve different problems. If you have made the architectural decision to choose Kafka over alternatives — or migrated from one to another — documenting that reasoning demonstrates senior-level distributed systems thinking that is rare and valuable.

Kafka integration code is typically Java, Python, or Scala — your language stats reflect your application stack. The presence of schema files (Avro .avsc, Protobuf .proto) in your repositories tells an experienced engineer that you understand Kafka's schema management layer, which is often the difference between a fragile and a production-grade streaming system.

For pinned repositories, a streaming data project with Kafka as the backbone — producer, consumer, schema definitions, and consumer group management — is a strong signal. Including Kafka configuration files (server.properties, docker-compose.yml with a Kafka cluster) lets visitors run your streaming pipeline locally, which is far more compelling than a README description alone.