Streaming Multimodal Data Platform

The chapters in this module, in reading order.

#	Chapter
00	Streaming Multimodal Data Platform — First-Principles Overview
01	Why the nightly warehouse makes a copilot lie — freshness as a budget, not a slogan
02	The replay log and the Monday flood — ingestion when consumers fall behind
03	Where raw bytes live and where queryable artifacts live — storage for a multimodal stream
04	Running models in the stream — ASR, vision, and embeddings when the unit of work is expensive and fallible
05	Finding the screenshot from a text question — multimodal indexing and incremental updates
06	How fresh is fresh enough — lambda, kappa, and the bill for data nobody queries
07	Tracing and erasing what flows through the pipe — lineage, quality, and PII across modalities
08	Where the pipe hits its limits — open problems, contested practices, and when streaming is the wrong tool