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AI Data Pipeline Ingest and Archive

AI Data Pipeline Ingest and Archive

2
posts
2026

This post introduces the foundational stages of an AI data pipeline, focusing on data ingest and archiving. It emphasizes the critical role of storage infrastructure in handling streaming ingestion, the importance of robust taxonomy for data organization and searchability, and the necessity of retaining comprehensive metadata (both as object tags and sidecar files) for reproducibility and downstream processing. The post also discusses how to expose metadata to training pipelines via manifests and highlights the specific storage requirements for GenAI workloads, including sustained high throughput, an always-hot architecture with no tiering, and free data movement, as exemplified by Backblaze B2 Overdrive.

2026

Enterprise Data Is 90% Unstructured: GenAI Needs Better Storage

8/6/2026

This post details the specific storage requirements for GenAI workloads, particularly RAG pipelines. It explains how traditional tiered storage and access patterns are ill-suited for the continuous, mixed read/write traffic and the need for immediate access to data for re-indexing. It introduces Backblaze B2 Overdrive as a solution that provides always-hot object storage with no tiering, no retrieval delays, and free egress, designed to meet these demands with high sustained throughput.

AI Data Pipeline 101: Ingest & Archive

7/29/2026

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).