A practical guide to building, scaling, and debugging Kafka Streams topologies, focusing on state stores, windowing, and production‑ready architecture.
A deep dive into building Kafka Streams topologies, from the DSL basics to production‑ready patterns, with concrete architecture diagrams and scaling strategies.
A deep dive into how stream engines decide what counts as late data, the mechanisms they expose, and best‑practice patterns for robust pipelines.
A deep dive into probabilistic sketches for cardinality estimation, covering theory, implementation, and operational best practices for modern observability streams.
Introduction Retrieval‑Augmented Generation (RAG) has rapidly emerged as a cornerstone for building intelligent applications that combine the expressive power of large language models (LLMs) with up‑to‑date, domain‑specific knowledge. While the classic RAG pipeline—retrieve → augment → generate—works well in centralized data‑center settings, modern use‑cases demand real‑time responses, low latency, and privacy‑preserving execution at the network edge. Enter vectorized stream processing: a paradigm that treats high‑dimensional embedding vectors as first‑class citizens in a continuous dataflow. By vectorizing the retrieval and similarity‑search steps and coupling them with a streaming architecture (e.g., Apache Flink, Kafka Streams, or Pulsar Functions), we can: ...