From Big Data to Large Models: Modern Data Paradigms and the Evolution of Database Technologies

The article begins by framing modern data technology as a means to "remove the shackles" of data, highlighting the 4V characteristics (volume, velocity, variety, value) that strain traditional data architectures and necessitate new paradigms.

It reviews the evolution of storage models, starting with relational databases and their ACID guarantees, then discusses the rise of NoSQL (key‑value, wide‑column, document, graph, time‑series) driven by Dynamo and BigTable, and the emergence of NewSQL that blends scalability with relational semantics.

Polyglot persistence is presented as a strategy that combines relational and NoSQL stores to leverage the strengths of each, while CQRS separates command and query responsibilities for consistency and performance.

The piece then shifts to big‑data analytics, describing Lambda architecture, HTAP, and the need for real‑time processing, before introducing embeddings as vector representations that capture semantic information from unstructured data.

Vector search technologies—including k‑NN, ANN, IVF, HNSW, PQ, and IVFPQ—are explained, along with similarity metrics (Euclidean, cosine, dot product) and their trade‑offs, illustrating how they enable efficient semantic retrieval of embeddings.

Hybrid retrieval combines traditional keyword/SQL search with vector‑based semantic search, requiring re‑ranking techniques such as RRF or LambdaMART to produce a unified result set.

Finally, Retrieval‑Augmented Generation (RAG) is described as the integration of search and large language models, allowing up‑to‑date, domain‑specific knowledge to be injected into LLM outputs, completing the transition from big‑data paradigms to generative AI workflows.