Data-Pipelines

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

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.

A framework for balancing Latency (system Completeness) against Verification (data Integrity) by effectively choosing between Speculative execution and Pessimistic consensus intervals.

A highly effective, local-first email auditing and automated cleanup tool designed to definitively identify noisy senders and deliver actionable, strictly privacy-preserving recommendations.

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.

An intensive end-to-end ETL processing example leveraging Apache Spark for large-scale parquet datasets; deeply focusing on strict schema handling, optimal partitioning, and reproducible aggregations.

A deep architectural comparison of streaming pipelines: evaluating Apache Beam's portable unified model (Java/DirectRunner) against Apache Flink's native API for stateful processing and fault tolerance.

An inherently scalable video ingestion and transcoding system architecture; asynchronously chunking heavy media, extracting actionable features, and steadily outputting adaptive bitrates via worker pools.