Kafka

Debezium is one of the most well-known CDC platforms, supporting real-time streaming of database changes to message queues such as Kafka

Kafka was open-sourced by LinkedIn in 2011, adopts a log storage model, and consumers independently advance their read progress by maintaining offsets.

Spring Boot、MyBatis、Kafka、Elasticsearch等企业级框架构成了国内绝大多数互联网公司的技术底座

Kafka's core design philosophy is 'the log is the truth': all events are durably written to an ordered, immutable distributed log, and consumers can independently control their consumption progress.

Through change data capture and daily audits, Vanguard migrated 35 producers to the gateway and onboarded 50 consumers across GraphQL, data warehouse views and Kafka.

Message queues and message brokers (such as RabbitMQ, Kafka and Redis Streams) can be used to wake agents, decoupling message arrival from agent execution, but they introduce additional operational complexity.

Message queues (such as Kafka and RabbitMQ) decouple producers from consumers and improve system resilience, but they also introduce new problems such as message backlog, out-of-order delivery, duplicate delivery, or dead-letter queues, and troubleshooting requires tracing the message lifecycle.

The first layer of legacy system refactoring requires systematic decomposition using domain-driven design (DDD), defining clear bounded contexts, and refactoring business domains into independent, lightweight microservices that communicate via secure REST APIs or event-streaming frameworks such as Kafka.

If SAP endpoints exceed 70%, the team has a pro-code (TypeScript/Java) background, an event-driven architecture is needed with no existing Kafka/Pulsar standard, and clean core is mandatory, then BTP is the right choice.

If SAP endpoints are below 30%, the team relies on low-code, a Kafka standard already exists, and API monetization and portal capabilities are needed, then alternatives to BTP should be seriously considered.

Options in the same category as Celery also include BullMQ (Redis-based) in the Node.js ecosystem, Python's RQ, cross-language Kafka consumer groups, and Temporal workflow workers.

These long-running process options (such as Celery, BullMQ, Kafka consumers, Temporal workers) were previously difficult to deploy directly on pure Serverless platforms, and often required a separate container hosting service.

Combining event triggering with incremental computation is the typical form of modern real-time data pipelines (such as those built on Kafka, Spark Structured Streaming or Delta Live Tables).

In streaming scenarios, dozens of Flink parallel subtasks committing a snapshot every 30 seconds while background compaction tasks are layered on top cause the conflict probability of Iceberg's optimistic concurrency control to rise sharply, which in turn triggers backpressure that propagates to the Kafka broker.

Multi-Agent collaboration has two orchestration modes: hierarchical orchestrator (a main Agent delegates tasks and verifies outputs) and swarm intelligence (Agents communicate through a shared message bus such as Kafka or NATS).

Kafka serves as the buffer queue for usage events, ensuring the upstream gateway's write path is not blocked by jitter in the downstream analytics store; ClickHouse is a columnar OLAP database, good at aggregate scans over massive numbers of events, but not at high-frequency single-row updates and transactions.

He-API's usage events are written to ClickHouse via Kafka, while billing, model and provider metadata land in PostgreSQL; the system comprises 15 services running on systemd, all in Go except one Next.js admin console; the middleware includes Postgres, Kafka, Redis and ClickHouse, and all 22 migration scripts are idempotent.

Traditional multi-agent architectures often introduce message queues (Kafka, RabbitMQ) or workflow engines (Temporal) to manage task dependencies and state transitions, at the cost of additional deployment, monitoring, and consistency debugging overhead.

Common backpressure implementations include buffer-based pull models (such as Kafka consumers) and semaphore-based push-pull combined models.

Manish's career started at Cognizant as an Advanced Java Developer, assigned to the supply chain management domain, and later, by self-studying data engineering technologies such as Python, Scala, Kafka, Airflow, and SQL/NoSQL and preparing for AWS certification, he obtained an internal transfer opportunity into the DevOps direction.