Managed Kafka

Assistance-operated Apache Kafka for event streaming, CDC, integrations, and real-time data pipelines

Managed Kafka is a supporting infrastructure add-on for teams building event-driven services, integration pipelines, CDC flows, analytics ingestion, or replayable event logs. Assistance operates the Kafka platform inside an agreed consulting or services boundary 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; 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

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.