Data Engineering for Neoclouds
Two sections under one cover. First, the practitioner playbook for running data pipelines on neocloud infrastructure — picking providers, training-data ingestion, orchestration, cost engineering. Then, the inside view — how DE teams at neocloud companies build the telemetry, billing, marketplace event streams, and capacity-planning data that make the platform work.
Section A · Orient
Section B · DE using neoclouds (practitioner)
01The DE Landscape for AI WorkloadsWhere data engineering fits in modern AI / GPU-cloud workflows. The handoff to ML.
02Choosing a Provider for Data PipelinesDecision framework for picking among Vast, RunPod, Lambda, CoreWeave, Together, etc.
03Training-Data PipelinesIngestion, cleaning, sharding, format choices (Parquet, WebDataset, etc.). Moving TBs to GPUs.
04Orchestration Across ProvidersAirflow / Dagster / Prefect / Argo against GPU clouds; multi-provider patterns; retries and idempotency on spot.
05Cost EngineeringTagging, allocation, spot vs reserved, checkpointing, budget guardrails. The DE's role in compute spend.
Section C · DE inside a neocloud
06Fleet TelemetryGPU-level metrics, node-level events, cluster-level health. The data backbone of a neocloud's operations.
07Billing & MeteringPer-second metering, dispute resolution, invoice generation. The pipeline that determines who pays whom.
08Customer Usage & Capacity DataUtilization fact tables, capacity-planning marts, forecasting inputs. The data that drives buy-vs-build decisions.
09Marketplace Event StreamsFor marketplace neoclouds: bid / preempt / listing events. The provider rating system's data source.
10Trust & Fraud DataVerification signals, hardware attestation, anomaly inputs. The data that protects the marketplace and the customer.
Section D · Synthesis