Fault-Tolerance

Architectural principles for reliable batch and streaming data pipelines; focusing on strict time semantics, exactly-once processing, optimal partitioning, observability, and reproducible states.

A deep architectural comparison of data processing pipelines: evaluating Apache Spark's batch ETL model against Apache Beam's portable unified model and Apache Flink's native API for stateful processing.

An optimized distributed caching architecture designed to drastically reduce backend I/O and accelerate VCS operations; intelligently caching heavy objects and hashes with ultra-low latency.

A highly resilient architectural design for a Google-scale web crawler; heavily focusing on breadth-first search (BFS), extensive DNS resolution caching, and polite handling of malicious domains.

Best practices for establishing robust observability using RED/USE metrics, contextual structured logging, distributed tracing, actionable alerting, and SLO-driven reliability engineering.

A highly scalable real-time streaming pipeline engineered to continuously ingest and process high-volume user event streams; gracefully handling late arrivals and robust fault tolerance protocols.

Design patterns for reliable microservice behavior under load; implementing strict request idempotency, non-blocking async I/O, robust circuit breakers, durable background queues, and observability.