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AI Data Pipeline Ingest and Archive

3 posts · Jun 2026 – Aug 2026

What this trail covers

Backblaze B2's AI Data Pipeline Ingest and Archive capability has evolved to emphasize the critical role of data collection and ingest in AI project success. The post details the five key phases of the AI data pipeline for model training, highlighting how storage infrastructure is integral to each stage. It stresses the importance of streaming ingestion for AI, requiring constant low-latency access, high rate limits, and high throughput. The capability now includes detailed guidance on implementing data governance and making informed storage infrastructure decisions to avoid costly egress fees and retrieval latencies incompatible with active AI pipelines, particularly for multimodal datasets. The focus is on aligning AI strategy with data strategy from the outset to ensure scalable AI programs.

All Backblaze trails
  1. Why GPUs Sit Idle: The AI Training Data Pipeline ProblemThis post details how Backblaze B2 Overdrive addresses the storage and throughput requirements for AI training data pipelines, enabling up to 1Tbps throughput and S3-compatible integration for PyTorch and TensorFlow pipelines. It highlights the importance of always-hot object storage with no tiering or retrieval delays, and a high-throughput layer to feed petabyte-scale GPU clusters continuously. The post also explains how this foundation benefits checkpointing by making writes and recoveries faster, reducing GPU time spent on these operations.
  2. How the Backblaze Drive Stats Dataset Powers Academic and AI/ML ResearchThis post details how the Backblaze Drive Stats dataset, originally an internal reliability tool, has become a valuable public resource for academic and AI/ML research, particularly in hard drive failure prediction. It highlights the dataset's characteristics (real-world origin, long time horizon, labeled failures, open license) that make it attractive to researchers. The post showcases specific academic papers and open-source projects that leverage Drive Stats, demonstrating its application in training transformer architectures, LSTMs, survival models, and gradient-boosted ensembles. It also discusses the practical considerations for using the dataset, such as download access, licensing, SMART attributes, and scope/coverage, including annualized failure rates. The contribution is in demonstrating the dataset's impact and utility as a benchmark for AI/ML research in a specific domain.
  3. Enterprise Data Is 90% Unstructured: GenAI Needs Better StorageThis post introduces Backblaze B2 Overdrive as a solution for the unique storage demands of GenAI and RAG pipelines. It highlights the limitations of traditional enterprise storage, particularly tiered storage and access patterns, in handling the continuous, mixed traffic and small object generation of RAG workflows. The post details the requirements for GenAI storage: sustained high throughput, always-hot architecture with no tiering, and free data movement. Backblaze B2 Overdrive is presented as meeting these requirements with its always-hot object storage, high sustained throughput, free egress, and S3 compatibility.
  4. AI Data Pipeline 101: Ingest & ArchiveThis post elaborates on the AI Data Pipeline Ingest and Archive capability by detailing the five key phases of the AI data pipeline for model training and emphasizing the integral role of storage infrastructure. It specifically focuses on the nuances of data ingest, differentiating it from data collection, and highlights the requirements of streaming ingestion for AI (low-latency access, high rate limits, high throughput). The post provides concrete guidance on implementing taxonomy with examples, retaining metadata through object tags and sidecar files, and exposing metadata to training pipelines via manifests. It also offers a more detailed breakdown of storage tiering decisions based on ingest type (batch, streaming, hybrid) and Backblaze B2's pricing advantages for AI workloads.
  5. Why AI Projects Stall: Data SilosThis post highlights the critical issue of data silos as a primary reason for AI project stalls. It emphasizes the need for centralizing multimodal data from disparate systems into a single storage repository for AI agents and engineers. The post details the strategic infrastructure decisions required, including inventorying existing data assets, establishing data governance early, and planning storage infrastructure with long-term costs (like hyperscaler egress fees and archive tier retrieval latencies) in mind. It frames proprietary data as a competitive advantage and advocates for aligning AI strategy with data strategy from the start, influencing infrastructure choices towards providers that support active data movement without penalties.