Streaming

A high-throughput streaming architecture for global ad click aggregation, ensuring exactly-once processing, robust deduplication, and sub-second reporting.

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.

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

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