Real-Time Systems That React, Not Wait

We design and implement event-driven architectures that enable real-time data flow across enterprise systems — replacing batch processes with instant, reliable event streams.

Apache KafkaConfluentRabbitMQAzure Event Hubs

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Deka Technology engineers working on event-driven architecture projects

500K+

Daily events processed

<1s

End-to-end event latency

90%

Reduction in stale-data errors

99.99%

Event delivery guarantee

 
THE CHALLENGE

Batch processing cannot keep up with real-time business

Enterprise integration has traditionally relied on batch processing — files transferred overnight, databases synchronized hourly, reports generated once a day. But business now moves in real time. Customers expect instant order confirmation. Supply chains need immediate visibility. Fraud detection cannot wait for a nightly batch run. Point-to-point integrations that poll for changes waste resources and add latency.

Event-driven architecture replaces this model with publish-subscribe patterns where systems emit events as they happen, and interested consumers react immediately. It decouples producers from consumers, enables real-time processing and scales naturally as event volume grows.

 
OUR APPROACH

Model & Design

We identify event-producing systems, define event schemas, model bounded contexts and design the event topology — topics, partitions, consumer groups and retention policies.

Build & Stream

We deploy the event platform (Kafka, RabbitMQ or cloud-native), implement producers and consumers, set up schema registry and configure dead-letter queues for error handling.

Monitor & Evolve

Event flow monitoring, consumer lag tracking, schema evolution management and performance optimization. The platform grows as new event sources and consumers are added.

 
CAPABILITIES

What we deliver

Apache Kafka Platform

Enterprise Kafka deployment with proper cluster sizing, partition strategy, replication configuration and security. Managed (Confluent, MSK, Event Hubs) or self-hosted based on requirements.

Event Schema Design

Schema definition using Avro, Protobuf or JSON Schema with a schema registry for version management. Backward and forward compatibility enforcement for safe schema evolution.

Stream Processing

Real-time event processing using Kafka Streams, Apache Flink or Azure Stream Analytics. Windowed aggregations, joins, filtering and enrichment for complex event processing scenarios.

Event Sourcing & CQRS

Event sourcing patterns where system state is derived from an immutable event log. Command Query Responsibility Segregation for optimized read and write paths in high-performance systems.

Legacy System Eventing

Adding event capabilities to legacy systems using change data capture (CDC), outbox patterns and database triggers. Legacy systems become event producers without code changes.

Batch processing too slow or systems out of sync?

Talk to an architect who designs Kafka and event-sourcing at scale.

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500K+

Daily events processed

<1s

End-to-end event latency

90%

Reduction in stale-data errors

99.99%

Event delivery guarantee

 
PROJECT SPOTLIGHT

EVENT-DRIVEN · REAL-TIME

Anadolu Efes

CHALLENGE

Anadolu Efes relied on hourly batch synchronization between SAP, warehouse systems and mobile field-sales applications. Sales teams worked with stale inventory data, leading to order rejections and customer frustration.

APPROACH

We implemented a Kafka-based event platform with CDC on SAP and warehouse databases. Inventory changes stream in real time to all consuming systems. Mobile apps receive push updates within seconds of any change.

RESULT

Inventory data freshness improved from hourly to sub-second. Order rejection rate due to stale data dropped by 90%. The event platform now processes 500K+ events daily.

500K+ daily events90% fewer rejectionsSub-second latency

 
USE CASES

Where we apply it

Confirm orders and trigger fulfillment in real time
Keep inventory consistent across every warehouse and channel
Flag suspicious transactions within milliseconds
Process millions of IoT events daily without lag
Stream customer activity to personalization engines instantly

 
TECHNOLOGY

Technologies

Apache KafkaConfluentRabbitMQAzure Event HubsAWS KinesisDebeziumApache FlinkAvroProtobuf

TECHNOLOGY PARTNERS

AWS

Microsoft

Red Hat

FAQ

Common questions about event-driven architecture

When should we use event-driven architecture vs. traditional API calls? +

Use events when multiple systems need to react to the same change, when you need real-time processing, or when you want to decouple producers from consumers. Use API calls for synchronous request-response patterns where the caller needs an immediate answer. Most enterprise architectures use both — events for data flow and APIs for queries and commands.

Should we use Kafka or RabbitMQ? +

Kafka is ideal for high-throughput event streaming, event sourcing and scenarios where events need to be replayed. RabbitMQ is better for traditional message queuing with routing, priority queues and request-reply patterns. For enterprise event platforms, we typically recommend Kafka. For application-level messaging, RabbitMQ may be more appropriate.

How do you handle event schema changes without breaking consumers? +

We use a schema registry (Confluent Schema Registry or equivalent) with backward compatibility enforcement. Consumers can process events with older or newer schemas without breaking. Schema evolution rules are enforced at the registry level — breaking changes are rejected before they reach production.

Can legacy systems participate in an event-driven architecture? +

Yes. We use change data capture (CDC) tools like Debezium to stream database changes from legacy systems as events. The legacy system does not need to be modified — we capture changes at the database level and publish them to Kafka topics. This is the most common pattern for legacy integration.

What about guaranteed delivery and exactly-once processing? +

Kafka provides at-least-once delivery with idempotent producers for exactly-once semantics within a single topic. For end-to-end exactly-once processing across systems, we implement idempotent consumers using deduplication keys. Dead-letter queues handle processing failures without blocking the main event stream.

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