Integrate LLMs into Your Data Pipelines (risingwave.com)

A practical blueprint shows how to augment, not replace, a high-performance streaming database like RisingWave with LLMs to tame unstructured data in real-time ETL pipelines. The guide argues for a hybrid architecture where RisingWave handles high-throughput structured stream processing while LLMs provide on-the-fly intelligence for language-heavy tasks: semantic extraction (turning free-text tickets into structured JSON), normalization and enrichment (standardizing product descriptions and suggesting categories/tags), and natural-language-to-SQL translation to enable self-service analytics. Key technical recommendations center on isolation and control: treat the LLM as a discrete pipeline stage, dispatch only the relevant text fields, validate outputs with schema checks, rule-based filters, or confidence thresholds, and define fallbacks (human review, nulling, or retry). Caching identical inputs reduces cost, latency, and nondeterminism. The guide also warns when not to use LLMs—high-volume trivial transforms, tasks requiring deterministic 100% accuracy (financial calculations), and ultra-low-latency millisecond use cases—because of cost, probabilistic behavior, and latency. Together these patterns enable engineers to combine RisingWave’s real-time throughput with LLMs’ semantic capabilities safely, improving data quality, discoverability, and user accessibility without compromising core pipeline performance.