01
High-throughput partitioning
Distributing events across partitions for parallel consumption with strict key-level ordering guarantees.
Technologies — Backend & systems
Apache Kafka event streaming & pipeline engineering
We design resilient event-driven architectures and streaming data pipelines with Apache Kafka and Confluent Cloud.
Core capabilities
Why we build with Apache Kafka
02
Schema Registry & Avro/Protobuf
Enforcing backward and forward schema compatibility across producer and consumer services.
03
Consumer group tuning
Tuning fetch sizes, commit intervals and heartbeat intervals to stop rebalancing storms.
Use cases
Where Apache Kafka fits
Event-sourced financial systems
Immutable audit ledgers recording every state mutation for regulatory compliance.
Real-time user activity tracking
Streaming high-volume clickstream events to real-time analytics dashboards.
How we staff it
Apache Kafka engineers you interview first
Seniority and experience are agreed in the proposal, and you interview every engineer before they start.
Working-hours overlap is agreed for each engagement and written into the statement of work — the shared window, who shifts hours, and how handoffs work outside it.
Technical FAQs
Frequently asked engineering questions
We increase max.poll.interval.ms, process messages in asynchronous worker pools, and use cooperative sticky rebalance assignors.
Kafka is designed for high-throughput event replayability, multiple consumer groups reading the same stream, and strict ordering by partition key.
Ecosystem
Related technologies
Planning an Apache Kafka project?
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