AI Data Pipeline 101: Ingest & Archive

What this post added

This post defines the initial stages of an AI data pipeline: ingest and archive. It details the requirements for streaming ingestion, including low-latency access, high throughput, and scalable storage. It introduces a structured approach to taxonomy for organizing data at ingest, providing an example schema and best practices for object naming and partitioning. The post also highlights the importance of retaining metadata, both as object tags and sidecar files, and suggests generating manifest files to expose this metadata to training pipelines. Finally, it offers considerations for choosing storage based on ingest type (batch, streaming, hybrid).