1. Data Streaming

I design and build real-time data streaming platforms that enable organizations to process, analyze, and react to data as it arrives — essential for modern cloud-native architectures.

Platform Setup & Infrastructure

Build production-ready streaming infrastructure on your preferred cloud:

• Apache Kafka — Distributed event streaming platform setup with high availability, schema registry, and proper topic design

• AWS Kinesis — Managed streaming services with auto-scaling and integration into the broader AWS ecosystem

• Google Pub/Sub / Dataflow — Serverless event ingestion and processing on GCP

Stateful Stream Processing

For workloads requiring complex event processing, windowing, and state management:

• Apache Flink — Low-latency, high-throughput stateful processing with exactly-once semantics and fault tolerance

• Apache Pinot — Real-time OLAP datastore for user-facing analytics with sub-second query latency at scale

Key Capabilities

Event-Driven Architecture	Design event schemas, implement CDC patterns, and build reactive microservices
Real-Time Analytics	Stream aggregations, windowed computations, and real-time dashboards
Scalability & Reliability	Horizontal scaling, replication strategies, and disaster recovery
Observability	Monitoring, alerting, and troubleshooting for streaming workloads

Explore related topics:

• Streaming Infrastructure

• Online Analytics

• Database Change Data Capture