Choosing Between Cloud, Edge, and Hybrid Architectures for Drilling Analytics
Choose cloud, edge, or hybrid deployment from software responsibility, latency, connectivity, data scope, support burden, and failure consequence.
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Drilling Data Architecture
Data models, pipelines, identity, storage, and quality controls for drilling information.
The Hidden Data-Engineering Work Behind Real-Time Drilling Analytics
Reliable real-time analytics starts with channel identity, units, clocks, quality, wellbore and BHA context, lineage, and recovery from delayed data.
Read article →Two Clocks, One Well: Modeling Time- and Depth-Indexed Data
Time and depth answer different drilling questions. Reliable alignment requires explicit grain, eligibility, direction, tolerance, survey authority, and provenance.
Read article →Why Drilling Data Is Not Just Time-Series Data
Drilling data needs time, depth, wellbore, rig-state, equipment, unit, and provenance context. Without that structure, valid measurements can still produce the wrong engineering answer.
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