Product teams adopting event-driven architecture
Decouple services, publish domain events, coordinate workflows, build projections, and preserve replayable operational history.
Kafka architecture, managed or self-managed clusters, topics, schemas, producers, consumers, Connect, stream processing, security, reliability, and operations
Apache Kafka engineering services
Rokad designs, builds, migrates, secures, and operates Apache Kafka and compatible managed event-streaming platforms across applications, data pipelines, and real-time systems.
Platform fit / 01
Designed for teams with a specific platform requirement.
Kafka is a distributed event log and streaming backbone, not merely a message queue. Rokad defines event ownership, topic and partition design, schemas, producers, consumers, delivery semantics, Kafka Connect, stream processing, security, capacity, observability, disaster recovery, platform lifecycle, and team operating contracts.
Data teams building real-time pipelines
Capture database changes, ingest events, process streams, validate schemas, route data, and deliver analytical or operational consumers.
Enterprises stabilising Kafka estates
Improve cluster architecture, capacity, security, schemas, connectors, lag, incidents, upgrades, recovery, cost, and ownership.
Implementation risks / 02
The platform problems Rokad is prepared to solve.
Topics are created without domain ownership or contracts
Names, keys, schemas, retention, partitions, ordering, compatibility, sensitivity, consumers, and lifecycle are undocumented.
Consumer lag is treated as one infrastructure metric
Backlog age, partition skew, processing time, errors, rebalances, dependencies, capacity, and business impact are not distinguished.
Exactly-once language hides end-to-end side effects
Broker transactions do not automatically make databases, APIs, files, emails, or downstream systems idempotent and recoverable.
Platform capabilities / 03
What Rokad can implement and operate.
01
Kafka use-case, event, workload, deployment, managed-provider, region, capacity, security, cost, and operating assessment
02
Cluster, broker, controller, rack or zone, storage, network, listener, replication, availability, scaling, and lifecycle architecture
03
Topic, partition, key, ordering, retention, compaction, replication, quota, naming, ownership, and data classification design
04
Producer, consumer group, offset, retry, dead-letter, idempotency, transaction, replay, backpressure, and failure engineering
05
Schema registry, Avro, Protobuf, JSON Schema, compatibility, evolution, validation, catalogue, lineage, and governance
06
Kafka Connect, source and sink connectors, CDC, custom connectors, stream processing, Kafka Streams, Flink, or compatible engines
07
Authentication, authorisation, encryption, secrets, observability, lag, capacity, upgrades, disaster recovery, incidents, and managed operation
Implementation system / 04
The architecture behind a dependable platform delivery.
Kafka platform
Managed or self-managed clusters, brokers, controllers, storage, zones, networks, identities, security, quotas, upgrades, and capacity.
Event contracts
Domains, topics, keys, partitions, ordering, schemas, compatibility, retention, compaction, sensitivity, ownership, and service levels.
Processing and integration
Producers, consumers, offsets, retries, dead letters, Connect, CDC, stream processing, state, replay, idempotency, and reconciliation.
Kafka operations
Broker health, under-replication, lag, throughput, latency, storage, quotas, rebalances, incidents, recovery, cost, and support.
Use cases / 05
Where this platform creates practical leverage.
Event-driven application architecture
Publish durable domain events, decouple services, build consumers and projections, coordinate processes, and support replay and recovery.
Database CDC and real-time data platform
Capture operational changes, apply schemas, transform streams, route consumers, preserve lineage, and monitor lag and quality.
Kafka cluster migration
Move from self-managed or another provider through topic, schema, producer, consumer, connector, replication, cutover, and rollback planning.
Kafka reliability and capacity programme
Improve partitions, replication, storage, quotas, producers, consumers, lag, observability, upgrades, recovery, and ownership.
Architecture / 06
Platform-specific engineering decisions and boundaries.
Partition key is a domain and scaling decision
Choose keys from ordering, entity affinity, distribution, hot-key risk, consumer parallelism, replay, and future growth requirements.
Schemas evolve under explicit compatibility policy
Define producer and consumer rollout, defaults, required fields, semantic changes, validation, ownership, and deprecation—not only syntax compatibility.
End-to-end processing remains idempotent and reconcilable
Track event identifiers, offsets, transactions, external side effects, retries, duplicates, checkpoints, replay, and business reconciliation.
Quality and governance / 07
Production controls are part of the implementation.
Reliable replication semantics
Schema changes, ordering, duplication, deletion, checkpoints, retries, backfills, idempotency, and reconciliation are designed per source and consumer.
Secure source-to-destination path
Credentials, network access, encryption, sensitive fields, logs, connectors, destinations, and operator permissions are controlled.
Observable pipeline operation
Sync state, lag, throughput, failures, records, schema drift, cost, incidents, and ownership remain visible and actionable.
Delivery / 08
A controlled path from assessment to operation.
Assess
Clarify the business outcome, current systems, platform constraints, data, integrations, risks, ownership, and measurable acceptance criteria.
Design
Define the platform architecture, workflow or storefront model, extensions, integrations, security, environments, and migration sequence.
Implement and validate
Build in controlled increments with testing, stakeholder review, observability, documentation, and platform-specific quality controls.
Launch and operate
Deploy safely, transfer ownership, monitor production behaviour, support users, and improve the implementation using operational evidence.
Typical platform deliverables
Kafka use-case, event, topic, schema, producer, consumer, cluster, security, capacity, cost, and risk assessment
Cluster, topic, schema, integration, processing, security, recovery, and operating architecture
Production clusters or managed services, topics, schemas, ACLs, quotas, and infrastructure automation
Producers, consumers, Connect, CDC, custom connectors, stream processing, retry, and replay implementation
Monitoring, lag, capacity, security, upgrade, backup or replication, disaster-recovery, and incident controls
Developer, data, platform, security, operator, governance, and handover documentation
Engagement models / 09
Use the delivery structure that matches the platform work.
Assessment and roadmap
A bounded review of the current platform, requirements, gaps, risks, architecture, and an executable next-stage plan.
Fixed-scope implementation
A defined integration, migration, storefront, application, workflow, or platform outcome with explicit acceptance criteria.
Embedded platform specialists
Specialists working alongside internal product, engineering, operations, marketing, data, or enterprise teams.
Managed platform evolution
Ongoing maintenance, releases, integrations, support, optimisation, governance, and roadmap execution after launch.
Related platforms and services / 10
Compare adjacent platforms or continue into the wider system.
01
Airbyte
Managed or self-hosted batch and CDC integration with standard and custom connectors.
02
Fivetran
Managed SaaS and database replication with automated connectors and warehouse delivery.
01
Data engineering
Pipelines, platforms, warehouses, analytics engineering, BI, and governed data operations.
02
Software development
Custom applications, backends, integrations, APIs, marketplaces, and enterprise systems.
03
Cloud and DevOps
Cloud architecture, delivery automation, observability, security, reliability, and platform operation.
FAQ
Apache Kafka engineering services
Platform scope, ownership, licences, data, integrations, security, migration, and long-term operation are clarified before delivery.
01Should we self-manage Kafka or use a managed service?
Should we self-manage Kafka or use a managed service?
We compare control, regions, networking, security, integrations, service guarantees, support, data movement, skills, lifecycle effort, portability, and total cost.
02Can Rokad design Kafka topic and schema standards?
Can Rokad design Kafka topic and schema standards?
Yes. We define domains, naming, ownership, keys, partitions, retention, compaction, schemas, compatibility, sensitivity, service levels, evolution, and deprecation.
03Can Rokad migrate an existing Kafka cluster?
Can Rokad migrate an existing Kafka cluster?
Yes. We inventory topics, schemas, producers, consumers, connectors, offsets, throughput, security, retention, dependencies, downtime, replication, validation, and rollback.
04Can Rokad operate Kafka after launch?
Can Rokad operate Kafka after launch?
Yes. Managed scope can cover cluster health, topics, schemas, connectors, lag, capacity, storage, security, upgrades, incidents, recovery, cost, and platform evolution.
Apache Kafka · Data pipeline development
Engineer Kafka around durable event contracts and end-to-end recovery.
Rokad can design the platform and topics, build producers and consumers, migrate clusters, and operate reliability, schemas, security, and capacity.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.