Managed Kafka
Assistance-operated Apache Kafka for event streaming, CDC, integrations, and real-time data pipelines
Managed Kafka is for teams building event-driven services, integration pipelines, CDC flows, analytics ingestion, or replayable event logs. Assistance operates the Kafka platform while your teams own event contracts, producers, consumers, and business semantics.
Best-fit use cases#
| Use case | Why Kafka fits |
|---|---|
| Event-driven microservices | Durable topics decouple producers and consumers while preserving event history |
| Change data capture | Stream database changes into analytics, search, caches, or downstream systems |
| Integration bus | Standardize movement of events between internal services and external systems |
| Real-time analytics | Feed clickstream, activity, telemetry, or operational data into processing systems |
| Log and audit pipelines | Retain ordered, replayable event records for downstream analysis |
What Assistance operates#
| Area | Included managed service responsibility |
|---|---|
| Provisioning | Cluster sizing, broker setup, storage configuration, network placement, secure defaults, and bootstrap details |
| Reliability | Replication settings, broker health, controller health, backup/retention strategy where applicable, and runbooks |
| Capacity | Partition, storage, throughput, consumer lag, and broker utilization monitoring |
| Maintenance | Kafka version lifecycle guidance, patch planning, maintenance windows, rolling upgrades, and rollback planning |
| Security | TLS, SASL, ACL model, service accounts, credential rotation support, and audit-friendly access practices |
| Governance | Topic naming, retention defaults, partition guidance, schema registry practices, and onboarding workflow |
| Support | Severity-based platform support and escalation for covered production clusters |
Kafka platform ownership is not event ownership
Assistance operates Kafka. Your teams own event contracts, producer behavior, consumer correctness, idempotency, schema evolution decisions, and downstream business processing. We can facilitate governance, but event semantics remain application ownership unless scoped separately.
Ownership boundary#
| Responsibility | Assistance owns | Customer owns |
|---|---|---|
| Kafka runtime | Broker operations, upgrades, monitoring, capacity, and platform incident triage | Producer and consumer application behavior |
| Topics | Guardrails, creation workflow, partition/retention recommendations | Topic purpose, event ownership, business retention requirements |
| Schemas | Registry operation and compatibility policy setup where included | Schema design, evolution approval, producer/consumer compatibility |
| Connectors | Platform operation when Kafka Connect is included | Source/sink credentials, data mapping, connector business behavior |
| Incidents | Broker/platform failures and service status | Bad events, poison messages, consumer bugs, duplicate handling |
Deployment options#
| Option | When to choose it |
|---|---|
| Assistance physical servers | Development, integration testing, lower-cost internal event platforms, and CI environments |
| Customer cloud account | Production platforms that must live near cloud-native applications and data services |
| Cloud-managed Kafka operations | Assistance operates MSK, Confluent, Azure Event Hubs Kafka API, or similar services where preferred |
| Hybrid | Development Kafka on Assistance infrastructure with production Kafka in cloud |
Reliability and support model#
| Topic | Managed Kafka approach |
|---|---|
| Availability | Multi-broker design and target availability scoped by topology, provider, and support tier |
| Durability | Replication factor, min in-sync replicas, retention, and compaction policies designed around data criticality |
| Recovery | Recovery expectations documented for broker loss, topic misconfiguration, and retention-related scenarios |
| Performance | Throughput, latency, partitions, storage, and consumer lag monitored continuously for covered services |
| Response | P1 response targets scoped in support agreement; 24/7 critical response available for covered production clusters |
Onboarding#
1. Streaming assessment#
We review event sources, consumers, throughput, retention, ordering needs, data sensitivity, replay requirements, expected growth, and integration targets.
2. Platform design#
Assistance proposes broker count, storage, replication, networking, ACLs, topic standards, schema registry approach, monitoring, and support model.
3. Producer and consumer onboarding#
We document connection details, topic request workflow, ACLs, schema rules, consumer lag dashboards, and runbook expectations for new services.
4. Operate and govern#
After go-live, we monitor broker health, lag, storage, throughput, and topic growth. Governance rules prevent unbounded retention, partition sprawl, and undocumented data ownership.
Supported capabilities#
- Apache Kafka broker clusters and KRaft/ZooKeeper lifecycle planning depending on version and environment
- Topic and partition governance
- Schema Registry with Avro, JSON Schema, or Protobuf where included
- Kafka Connect operations for scoped source/sink connectors
- Mirror or replication patterns for migration and disaster recovery where appropriate
- Metrics and alerting for brokers, topics, partitions, and consumers
Not included by default#
- Designing every event contract or business data model
- Rewriting producers or consumers for idempotency and compatibility
- Guaranteeing delivery semantics for application code outside Kafka
- Unlimited retention, topics, partitions, connectors, or throughput outside the plan
- Owning downstream data correctness after consumers process events
Related products#
- Managed PostgreSQL — Common source for CDC and transactional events
- Managed OpenSearch — Search and analytics sink for event streams
- Managed Prometheus — Kafka metrics, alerting, and consumer lag visibility
- SRE as a Service — Incident operating model for event-driven systems
Getting started#
Request a Kafka assessment. We will map producers, consumers, topics, retention, ownership, and support requirements before proposing a managed streaming platform.
Request Kafka assessment →Frequently asked questions#
Is Kafka the right choice for simple background jobs? Not always. Redis queues, a database-backed queue, or a managed cloud queue can be simpler. Kafka is best when you need durable replayable streams, multiple consumers, and event history.
Who creates topics? We define a topic request workflow. Assistance can create topics and enforce defaults, while your team identifies ownership, purpose, retention, schema, and consumers.
Can you operate MSK or Confluent instead of self-hosted Kafka? Yes. We can operate cloud-managed Kafka services in your account or tenancy when that is the better fit.
How do you handle schema changes? Schema governance is part of onboarding when Schema Registry is included. Your teams own schema design and compatibility decisions; Assistance operates the registry and policy mechanism.
What SLA applies to Kafka? Availability and response targets are scoped by cluster topology, provider dependencies, and support tier. We define these before production onboarding.