An inverted index maps terms to documents, allowing retrieval without scanning every document. Analyzers normalize text through tokenization, case normalization, stemming, synonyms, and language-specific rules. These choices change recall and precision.

Ranking may combine lexical relevance such as BM25 with freshness, field weights, popularity, personalization, and business constraints. Vector similarity can retrieve semantically related material but may miss exact terms or violate product expectations unless combined with lexical retrieval and filters.

Evaluate relevance with representative queries and labeled judgments, not just latency. Monitor zero-result searches, click-through, index freshness, and query failures. Search is a user-facing product feature, and a fast response can still be irrelevant or unauthorized.

OLTP systems handle operational transactions with integrity and low-latency requirements. OLAP systems perform large scans and aggregations. Heavy analytics on a transactional primary can compete for CPU, memory, and I/O, so teams often replicate or transform data into analytical systems.

Warehouses store curated analytical data; data lakes store diverse raw data; lakehouse approaches add transactional table metadata and governance on lake storage. Every platform needs schema evolution, lineage, data-quality checks, access controls, and freshness objectives.

Metric discrepancies can result from different definitions, event-time windows, duplicate handling, or late-arriving corrections. Define authoritative metric logic and publish it consistently across dashboards.

A RAG pipeline ingests documents, cleans and chunks text, generates embeddings, stores vectors and metadata, retrieves candidates, reranks them, and supplies selected context to a language model. Chunk size, overlap, metadata filters, embedding choice, and reranking all affect retrieval quality, latency, and cost.

Vector similarity is not authorization. Apply tenant and document access filters before context reaches the model. Treat retrieved text as untrusted because it may contain prompt injection. Embeddings and indexes are derived data and need refresh/rebuild plans when source documents or embedding models change.

Evaluate retrieval recall separately from answer quality. Track source grounding, latency, token use, cost per successful task, and stale-document behavior. Provide citations or source snippets when users need to verify answers.