Data-Pipelines

7 items

Retrieval & RAG — Data-Pipelines , Embeddings , Indexing and +2 more
Operational principles for robust search retrieval and RAG pipelines; focusing on hybrid lexical-semantic retrieval techniques, long-term embedding model stability, automated ranking evaluation, and privacy-aware indexing.
Web App — Data-Pipelines , Monitoring , Queuing and +1 more
Guiding principles for production-ready web apps using 12-factor methodologies; encompassing stateless scaling, durable media handling, robust background processing, CI/CD, and deep observability.
Monitoring & Observability — Data-Pipelines , Fault-Tolerance , Monitoring and +2 more
Best practices for establishing robust observability using RED/USE metrics, contextual structured logging, distributed tracing, actionable alerting, and SLO-driven reliability engineering.
Data Pipelines — Data-Pipelines , Fault-Tolerance , Parallelization and +2 more
Architectural principles for reliable batch and streaming data pipelines; focusing on strict time semantics, exactly-once processing, optimal partitioning, observability, and reproducible states.
Intervals & Constraints — Analytics , Data-Pipelines , Integrity and +1 more
A framework for balancing Latency (system Completeness) against Verification (data Integrity) by effectively choosing between Speculative execution and Pessimistic consensus intervals.
Model Serving & Inference — Data-Pipelines , Machine-Learning , Monitoring
Principles for highly available production ML inference; utilizing immutable model registries, dynamic request micro-batching, safe canary rollouts, graceful fallback degradation, and efficient GPU memory management.
Data Processing Architectures — Data-Pipelines , Fault-Tolerance , Parallelization and +2 more
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.