Production event streaming infrastructure
Trusted by 100+ innovative teams
What we build
From cluster setup and stream processing to event sourcing, CQRS, and real-time analytics pipelines, we build streaming infrastructure that scales to billions of events per day.
Built for teams like yours
How we deliver
Map your workflows, identify high-impact opportunities, and quantify ROI potential.
Build a focused MVP for your highest-impact use case in 4-6 weeks.
Harden, monitor, and expand — leveraging existing infrastructure for each new capability.
4-8 weeks
pilot to production
95%+
milestone adherence
99.3%
SLA stability
Apache Kafka & Real-Time Streaming Implementation
Use the same rollout pattern we apply in production programs: architecture review, risk controls, and measurable milestones from pilot to scale.
4-8 weeks
pilot to production timeline
95%+
delivery milestone adherence
99.3%
observed SLA stability in ops programs
Deep dive
Apache Kafka has become the default event backbone for real-time architectures. Not because it's the only option — Pulsar, Redpanda, and managed Kinesis all exist — but because Kafka's combination of throughput, durability, ordering guarantees, and ecosystem (Kafka Streams, Connect, Schema Registry) is hard to beat at production scale.
We help engineering teams design and run Kafka platforms that hold up under real production load — not Kafka demos, not "we have Kafka" PowerPoints, but Kafka clusters that are part of the team's daily operations.
The first set of decisions in a Kafka deployment is topology. Wrong choices here haunt the cluster for years.
We have inherited Kafka clusters where the original sizing was off and the cost of re-partitioning a high-volume topic was measured in weeks. Get this right at the start.
Kafka supports exactly-once semantics within a Kafka cluster: a producer can write to multiple topics atomically, and a consumer can process and produce in a single transaction. This is genuinely valuable for streaming pipelines that move data within Kafka.
The caveat: exactly-once within Kafka does not extend to external systems by default. A consumer that writes to PostgreSQL is responsible for its own idempotency. Patterns we use:
End-to-end exactly-once across all systems requires architectural choices, not just configuration.
A Kafka cluster without a schema registry becomes a graveyard of "what does this field mean" investigations within a year. The registry is non-negotiable for any team beyond the smallest.
We standardize on:
Beyond format choice, the registry should enforce forward and backward compatibility by default. Breaking changes are gated by explicit overrides. This is the difference between a Kafka cluster you can evolve and one that becomes legacy within two years.
Kafka topics by themselves carry events; stream processors transform them.
The choice depends on team and workload, not Kafka itself.
Kafka is the natural backbone for CQRS (Command Query Responsibility Segregation) and event-sourced architectures. The patterns are powerful but worth using deliberately.
We help teams pick the right level of these patterns for their actual problem, not the level the architecture diagram looks coolest at.
Production Kafka requires real operations:
These are the daily operations of Kafka teams. We hand off runbooks and dashboards alongside the cluster.
For most engagements, we typically engage in three modes:
We do not parachute in to write code and leave. Every engagement ends with the client team's engineers operating what we built.
Kafka rewards teams that take it seriously and punishes teams that treat it as a write-only message broker. The investment compounds across every downstream system that consumes from it.
Confluent Cloud is ideal for teams that want managed infrastructure with minimal ops overhead. Self-managed Kafka (on Kubernetes with Strimzi) gives you more control and can be cheaper at scale. We help you evaluate based on your team size, traffic volume, compliance requirements, and budget.
Kafka is designed for high-throughput, ordered event streaming with replay capability — ideal for event sourcing, log aggregation, and real-time analytics. RabbitMQ is better for traditional message queuing with complex routing. SQS is simplest for basic async processing. We recommend Kafka when you need event replay, high throughput, or stream processing.
Kafka handles millions of messages per second in production at companies like LinkedIn, Uber, and Netflix. The key is proper cluster sizing, partition strategy, and consumer group design. We benchmark against your expected throughput and design for 3-5x headroom.
A basic Kafka cluster with 2-3 producer/consumer services takes 3-4 weeks. A full event-driven architecture with stream processing, schema management, monitoring, and multi-service integration typically takes 8-14 weeks depending on the number of services and data sources.
Yes. Redpanda is a Kafka-compatible alternative written in C++ that offers lower latency and simpler operations. Apache Pulsar provides multi-tenancy and geo-replication natively. We evaluate your requirements and recommend the best fit — we're not locked into any single platform.
Explore related services, insights, case studies, and planning tools for your next implementation step.
Delivery available from Bengaluru and Coimbatore teams, with remote implementation across India.
Case Studies
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Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002