Apache Kafka Event Streaming & Real-Time Pipeline Architecture
Wolk Inc designs and implements Apache Kafka event streaming systems: MSK and Confluent Cloud deployment, event-driven architecture, Kafka Streams processing, Schema Registry, and full production monitoring. Built for the throughput and reliability your real-time systems require.
MSK · Confluent
Managed Kafka Platforms
Strimzi
Self-Hosted on Kubernetes
EOS
Exactly-Once Semantics
CDC
Debezium Change Data Capture
Kafka Consulting Deliverables
Event-Driven Architecture Design
Topic design and partitioning strategy, event schema design using Apache Avro or Protobuf with Schema Registry enforcement, producer and consumer configuration (batching, compression, idempotency, exactly-once semantics), and domain-event boundary definition aligned to your service architecture and bounded contexts.
Kafka Cluster Configuration & Operations
Kafka cluster deployment on Amazon MSK, Confluent Cloud, or self-managed on Kubernetes (Strimzi operator). Broker sizing, replication factor, retention policy, and rack-awareness configuration. Kafka MirrorMaker 2 setup for cross-region replication or disaster recovery. Kafka Connect source and sink connector configuration for database CDC (Debezium), S3, Snowflake, BigQuery, and Elasticsearch.
Stream Processing with Kafka Streams & ksqlDB
Stateful and stateless stream processing with Kafka Streams: windowed aggregations, joins, and enrichment pipelines. ksqlDB push and pull query implementation for operational analytics. Stream-table duality patterns for event sourcing. Dead-letter queue design and consumer error handling strategy.
Monitoring & Operational Readiness
Kafka metrics collection via JMX exporter into Prometheus + Grafana. Consumer lag monitoring with Burrow or kafka-consumer-lag-monitoring. End-to-end latency tracking across producer → topic → consumer. Alerting rules for consumer lag thresholds, broker under-replicated partitions, and producer error rates. Runbooks for partition rebalancing, consumer group reset, and topic compaction issues.
Kafka Stack Coverage
Production Kafka. Schema-First Design.
Kafka Streaming Questions
When does an architecture need Kafka rather than a simpler message queue (SQS, RabbitMQ)?▾
Kafka is the right choice when you need: (1) event log replay — consumers can re-process historical events from a retention window; (2) high throughput — Kafka handles millions of events per second with very low latency; (3) multiple independent consumers of the same event stream without coupling producers to consumers; (4) ordered processing within a partition; (5) stream processing with joins and aggregations. SQS and RabbitMQ are simpler and sufficient for task queue patterns. Kafka is worth the operational overhead when your system genuinely needs event streaming semantics.
Should we use Amazon MSK, Confluent Cloud, or self-managed Kafka?▾
Amazon MSK is the lowest-friction choice for AWS-native teams: managed brokers, IAM authentication, and VPC integration. Confluent Cloud adds Schema Registry, ksqlDB, and Kafka Connect as managed services — valuable if you need those features without operating them yourself. Self-managed Kafka on Kubernetes (Strimzi) is the right choice for teams with strict data sovereignty requirements or very high throughput where managed service economics become unfavourable. Wolk Inc recommends based on your cloud environment, compliance requirements, and operational capacity.
How does Wolk Inc implement exactly-once semantics in Kafka?▾
Exactly-once semantics (EOS) in Kafka requires: (1) idempotent producers — enabled via `enable.idempotence=true`; (2) transactional producers for atomic multi-topic writes; (3) read-committed isolation level on consumers. Kafka Streams enables EOS via the `processing.guarantee=exactly_once_v2` configuration. For Kafka Connect sinks, EOS depends on the sink connector's support and the target datastore's transaction model. Wolk Inc implements EOS where it is genuinely needed and documents the performance trade-offs — EOS adds latency and reduces throughput compared with at-least-once delivery.
How do you handle schema evolution in a Kafka-based system?▾
Wolk Inc implements Confluent Schema Registry (available with Confluent Cloud, MSK, or self-hosted) to enforce schema compatibility rules. We recommend BACKWARD compatibility by default: new consumer versions can read messages produced by older producers. Schema evolution rules (adding nullable fields, deprecating fields with defaults) are defined in the Schema Registry and enforced at producer registration time. This prevents breaking changes from reaching production consumers without explicit version management.
What is consumer lag and how do you monitor it?▾
Consumer lag is the difference between the latest offset produced to a Kafka topic partition and the latest offset committed by a consumer group. High lag means your consumer is falling behind the producer rate — either due to processing bottlenecks or consumer failures. Wolk Inc implements Burrow (Kafka consumer lag evaluation service) or the Kafka Exporter for Prometheus to track per-consumer-group lag across all partitions, with Grafana dashboards and alerting thresholds. Lag alerting is one of the most important operational metrics for any Kafka-based system.