Build resilient, event-driven architectures and streaming analytics engines designed for operational efficiency. Throughput and latency targets are workload-specific — we size them against your actual event volume, payload size, and durability requirements during discovery, rather than quote a generic number that may not hold for your traffic shape.
# Illustrative example: a Strimzi-managed Kafka topic feeding a Flink
# Kubernetes Operator job. Both CRDs are from their respective upstream
# projects (strimzi.io, flink.apache.org) — adapt names/sizing to your cluster.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
name: orders-events
labels:
strimzi.io/cluster: production-kafka
spec:
partitions: 12
replicas: 3
config:
retention.ms: 604800000
min.insync.replicas: 2
---
apiVersion: flink.apache.org/v1beta1
kind: FlinkDeployment
metadata:
name: orders-stream-processor
spec:
image: flink:1.19
flinkVersion: v1_19
job:
jarURI: local:///opt/flink/usrlib/orders-processor.jar
parallelism: 8
upgradeMode: savepointWho this is for
Teams with event volume or latency requirements outgrowing a request/response or batch architecture, or already running Kafka/Flink/Redpanda but struggling with cluster sizing, consumer lag, or an unclear path through a migration.
Expected outcomes
A streaming platform sized and configured for your actual traffic — not a default configuration — with defined schemas/contracts between producers and consumers, explicit ordering and processing-semantics guarantees (at-least-once vs. exactly-once, where that distinction matters to your use case), and a tested plan for backpressure and replay.
Scope & deliverables
We work at whichever layer your bottleneck actually is: Kafka cluster topology and tuning, Flink stateful stream processing jobs, or a low-latency Redpanda migration. Any throughput/latency figures we propose are tied to a specific measurement: broker acknowledgement latency, end-to-end processing latency, or application-observed latency are different numbers, and we’ll be explicit about which one we’re targeting, at what percentile, under what test conditions.
Our approach
Discovery establishes your current topology, schemas, and failure modes; we then propose a target design and validate it against a representative load test before any production cutover. Migrations include an explicit rollback path and a plan for replaying or reconciling events if a cutover needs to be reversed.
Client responsibilities & exclusions
You’ll need to provide representative traffic patterns (or allow us to generate them) for load testing — we won’t sign off on a sizing recommendation based on guesswork. This engagement delivers a validated pipeline design and implementation; ongoing 24/7 operational coverage is a separate, explicitly scoped conversation.
Related: High-Performance Data Lakehouses