- contact@verticalserve.com
Assess, build, migrate, or operate Kafka and Flink at enterprise scale — Confluent, Apache, MSK, and Redpanda, with automated migration tooling and a principal-led squad working inside your environment.
We help enterprises harness real-time data streaming across Confluent Cloud and Platform, Apache Kafka, AWS MSK, and Redpanda — from the first architecture decision through migration, hardening, and day-two operations.
We assess your current streaming landscape, identify where Kafka genuinely earns its place, and build a roadmap tied to business outcomes. You get a clear view of workloads, topology, cost, and risk before anyone provisions a cluster.
Confluent Cloud, Confluent Platform, MSK, or self-managed Apache — the right answer depends on your compliance posture, ops maturity, and cost profile. We design the topic taxonomy, partitioning, replication, and multi-region strategy to match.
Flink, Kafka Streams, and ksqlDB pipelines built for correctness — windowing, watermarks, state store sizing, exactly-once semantics, and the reprocessing strategy you will eventually need when business logic changes.
Kafka Connect at scale, Debezium CDC off transactional databases, and custom connectors where the ecosystem falls short. We handle schema evolution through Schema Registry so producers and consumers can change independently.
Automated migration from self-managed or legacy Kafka onto Confluent Platform or Cloud — consumers, producers, Streams apps, and Flink jobs. We run parallel and reconcile before cutover, so the switch is evidence-based rather than hopeful.
RBAC and ACL design, mTLS and OAuth, encryption in transit and at rest, topic naming and ownership standards, schema compatibility policy, PII handling, and audit trails that satisfy a regulator rather than merely existing.
Consumer lag SLOs, broker and partition health, quota policy, alerting that distinguishes noise from incidents, and disaster recovery you have actually rehearsed. Backed by our own health-check tooling.
GitOps for topics and ACLs, Terraform and Confluent Operator provisioning, CI/CD for streaming applications, and environment promotion. The platform becomes reproducible infrastructure instead of manual console work.
Right-sizing, retention and tiered storage policy, compression tuning, partition rationalization, and cluster consolidation. Streaming spend is usually 30–50% larger than it needs to be, and it is almost always fixable.
Workshops, paired delivery, and runbooks so your team owns the platform when we leave. Knowledge transfer is a deliverable with a date on it, not a hope.
Kafka into Delta and Iceberg with exactly-once guarantees, schema evolution, and compaction that keeps query performance stable. Delivered jointly with our Databricks POD when the sink side needs building too.
Ongoing platform operations, on-call support, upgrade management, and capacity planning for teams that want the capability without building a dedicated streaming practice in-house.
We do not start from an empty cluster. These are our own tools, already hardened in production, adapted to your environment rather than rebuilt from scratch.
Centralized management of Kafka resources across clusters and environments — topics, ACLs, quotas, and ownership in one governed control plane.
Design and provision correctly-sized clusters in minutes, with partitioning, replication, and retention derived from your actual throughput and durability requirements.
Intelligent operations — anomaly detection on lag and throughput, automated remediation for common failure modes, and capacity forecasting.
Automated production health check across configuration, security posture, replication, and client behaviour. Delivered as a prioritized findings report.
Governed schema management on top of Schema Registry — compatibility policy, review workflow, and change history that teams can self-serve against.
Data loss prevention for streaming payloads plus a streaming data catalog, so you know what sensitive data is flowing through which topics and who consumes it.
A Kafka POD is 3–6 senior streaming engineers working inside your environment, on your backlog, with your team in the room.
Cluster inventory, workload profile, security and cost baseline, and a health check across configuration and client behaviour. Output is a target topology your team has signed off on.
Platform provisioned as code, governance model in place, schema registry policy set, CI/CD wired, and the first production topic flowing end to end.
Pipelines, connectors, and stream processing delivered in two-week increments. Migrations run parallel with reconciliation before any cutover.
Runbooks, SLOs, alerting, DR rehearsal, and paired delivery with your engineers until they are running it. You keep the code, the IaC, and the documentation.
Most clients begin with a health check or assessment and convert into a full POD once the roadmap is agreed.
2–3 weeks, 2 principals
8–16 weeks, 3–6 engineers
Rolling, scales up and down
A few of the production engagements we’ve shipped. Browse the full case-study library for all 50+.
AI/ML, Healthcare, Anomaly Detection
Cloud AWS, Healthcare, Kafka
GitOps, Healthcare, Kafka