Data Engineering

The Disruption of Naive RAG & Enterprise GraphRAG Era

Prerequisite: Review the previous module in the ai-data-engineering-pipeline series before proceeding. Executive Summary: The Disruption of Naive RAG and the GraphRAG Era Answer-first: Naive RAG collapses in enterprise environments due to relational blindness, unstructured document chunk destruction, and lack of fine-grained access control. Modern AI architectures combine Knowledge Graphs with vector search (GraphRAG) and event-driven data ingestion to deliver 100% data freshness, 38% higher retrieval precision, and deterministic row-level security. Architecting this pipeline enforces sub-50ms P99 latency guarantees, OpenTelemetry GenAI semantic conventions, and. ...

Data Engineering SFT: NEFTune & SemDeDup | SLM Playbook

Prerequisite: Familiarity with the concepts introduced in Executive Summary. Review it first if the terminology in this part is unfamiliar. ← Series hub ← Previous | Next → Answer-first: Supervised Fine-Tuning (SFT) data quality determines downstream model capabilities; applying NEFTune noise injection during training improves conversational quality by up to 20%, while SemDeDup vector clustering prunes 30%–50% of redundant data to cut GPU training hours nearly in half without losing model accuracy. Implementing this architecture enforces sub-50ms P99 latency guarantees, strict component isolation, and automated observability pipelines required for. ...

E-commerce Data Ingestion & Atomic Chunking Pipelines

Prerequisite: Familiarity with the concepts introduced in Part 1 — Golang Orchestration. Review it first if the terminology in this part is unfamiliar. Data Ingestion & Atomic Chunking Product Data: Semantic Catalog Pipelines Answer-first: Data ingestion and atomic product chunking processes catalog updates into dense vector embeddings, maintaining vector index freshness and search accuracy. Implementing this architecture enforces sub-50ms P99 latency guarantees, zero-allocation memory pooling with Go 1.24 unique.Handle, and fault-tolerant Dapr 1.15 component orchestration for resilient production scaling. This design guarantees sub-50ms P99 latency bounds and zero-allocation memory pooling. ...