Cloud Computing & Distributed Systems

An applied exploration of cloud-native and distributed systems, focusing on consistency, fault tolerance, and observable behavior under failure.

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☁️ Project Snapshot

Focus: Cloud-Native & Distributed Systems Scope: Databases, Messaging, Consistency, Fault Tolerance Environment: Docker, Kubernetes, Apache Cassandra, Apache Kafka

🧠 What This Project Explores

How distributed systems coordinate state and communication at scale
How consistency, availability, and reliability trade off under failure
How fault injection reveals real system behavior beyond theory

🧪 What I Built & Analyzed

Deployed containerized services using Docker and Kubernetes
Built and tested distributed data services backed by Apache Cassandra
Configured a distributed message queue using Apache Kafka and ZooKeeper
Implemented producers and consumer groups with dynamic partition handling
Injected controlled failures (chaos-style fault injection) to test resilience

🔁 Distributed System Behavior

Tunable consistency and availability trade-offs in Cassandra
Leader election, replication, and recovery in Kafka clusters
Consumer group rebalancing and partition reassignment under failure
Effects of pod crashes, network disruption, and restarts in Kubernetes
Differences between partial failures and full process termination

🛠 Core Technologies & Concepts

Containers, pods, services, and orchestration
Distributed databases and eventual consistency
Message queues and stream processing
Consumer groups, partitions, and replication
Chaos Engineering and fault injection
Resilience, retries, and self-healing systems

🎯 Skills Demonstrated

Reasoning about consistency, availability, and fault tolerance
Designing and debugging distributed systems in Kubernetes
Building reliable messaging pipelines with Kafka
Testing system behavior under real failure scenarios
Translating theory into observable, production-like behavior