Kafka Consulting

Kafka is a distributed event streaming platform used to publish, store, and process high-volume data streams in real time. It is commonly used by engineering teams building data pipelines, microservices, and analytics systems that need reliable, scalable communication between producers and consumers.

Kafka typically runs as a clustered service (often on Kubernetes or managed cloud offerings) and acts as a central backbone for event-driven architectures, enabling systems to react to changes and share data without tight coupling. For more details, see Apache Kafka.

• Durable event logs for replayable, ordered streams
• High-throughput pub/sub messaging across many services
• Stream processing and enrichment workflows
• Integration with connectors for databases, storage, and SaaS systems

Message Queues are a useful tool that can integrate easily and empower your project with many benefits, such as:

• Decoupled communication between applications
• Improved scalability and reliability
• Asynchronous processing and handling of messages
• Load balancing and message prioritization
• Durable storage of messages for guaranteed delivery
• Supports processing of large volumes of messages.

Kafka is a distributed event streaming platform used to publish, store, and process high-volume event data with low latency. It is typically chosen as a durable backbone for data pipelines and event-driven architectures where multiple systems need to consume the same stream reliably.

• High-throughput ingest and fan-out, enabling many producers and consumers to share event streams without point-to-point coupling.
• Durable, replayable log storage, allowing consumers to reprocess historical events for backfills, audits, and recovery.
• Horizontal scalability via partitioning, so throughput can increase by adding brokers and distributing partitions.
• Fault tolerance through replication and leader election, helping maintain availability and durability during broker failures.
• Ordering guarantees within a partition, supporting keyed processing patterns such as per-customer or per-entity event handling.
• Consumer groups for parallelism, enabling scalable microservices and stream processing workloads with coordinated consumption.
• Configurable retention and log compaction, supporting both time-based retention and latest-state topics for change streams.
• Connector ecosystem with Kafka Connect, standardizing ingestion and delivery to databases, warehouses, object storage, and SaaS systems.
• Stream processing options like Kafka Streams and ksqlDB, enabling near-real-time transformations, joins, and aggregations close to the data.
• Operational and governance controls such as quotas, ACLs, and observability integrations, supporting multi-tenant production use.

Kafka is a strong fit when event volume, consumer fan-out, or replay requirements exceed what typical message queues provide. Trade-offs include operational complexity around capacity planning, partitioning strategy, and tuning, plus the need for disciplined schema and compatibility management to avoid breaking downstream consumers.

Common alternatives include Apache Pulsar, RabbitMQ, Amazon Kinesis, and Google Pub/Sub. For implementation details and configuration guidance, see Kafka documentation.

Our experience with Kafka helped us develop repeatable delivery patterns, automation, and operational guardrails that make event-streaming platforms easier to implement, scale, and operate reliably in production.

Some of the things we did include:

• Designed Kafka reference architectures for domain-aligned and multi-tenant streaming, including topic taxonomy, partitioning strategy, and schema/versioning governance.
• Built and stabilized Kafka clusters on Kubernetes, including broker lifecycle automation, safe rolling upgrades, and capacity planning based on throughput, retention, and failure scenarios.
• Implemented secure connectivity and access controls (TLS/mTLS, SASL, ACLs, quotas) and aligned authorization with enterprise IAM patterns for auditable producer/consumer access.
• Delivered observability for Kafka and client applications using Prometheus metrics, dashboards, and alerting focused on consumer lag, under-replicated partitions, controller health, and broker resource saturation.
• Hardened reliability with HA/DR patterns such as multi-AZ deployments, replication and min.insync.replicas tuning, controlled leader movement, and tested recovery runbooks with clear RPO/RTO targets.
• Migrated workloads from legacy message brokers and point-to-point integrations to Kafka, including cutover planning, dual-write/dual-read strategies, backfill approaches, and compatibility testing to reduce production risk.
• Automated Kafka operations with CI/CD and GitOps workflows for topics, ACLs, quotas, and connector configurations to improve change traceability and reduce manual drift.
• Optimized performance and cost through broker sizing, disk and network tuning, retention and compaction policies, and load testing under realistic traffic profiles.
• Integrated Kafka with downstream processing and analytics platforms, including Spark streaming workloads and curated pipelines in Databricks.
• Enabled standardized producer/consumer practices through documentation, templates, and enablement sessions to improve developer experience and on-call response.

This experience helped us accumulate significant knowledge across Kafka use cases—from greenfield builds to migrations and stabilization—and enables us to deliver high-quality Kafka solutions that are reliable, scalable, and maintainable for client teams.

Some of the things we can help you do with Kafka include:

• Assess your current event-streaming architecture and deliver a findings report with prioritized fixes for reliability, scalability, and operability.
• Create an adoption roadmap covering topic strategy, partitioning, retention, schema governance, and operating model across teams.
• Design and implement Kafka clusters for production, including capacity planning, HA/DR, and safe rollout strategies.
• Harden security and compliance with TLS, authentication/authorization, network controls, and guardrails for multi-team usage.
• Improve performance and cost efficiency by tuning brokers, producers/consumers, batching, compression, and storage/retention policies.
• Operationalize Kafka with Infrastructure as Code, CI/CD, and repeatable runbooks for upgrades, scaling, and incident response.
• Establish observability with actionable metrics, logs, and alerting to reduce MTTR and prevent consumer lag and outages.
• Troubleshoot production issues such as throughput bottlenecks, rebalances, replication delays, and data loss risks.
• Enable teams with hands-on training and best practices for event design, consumer patterns, testing, and safe schema evolution.