Platform capabilities
See how dataXchange brings the AdMultaDB backbone, Native acceleration, stream processing, and analyst-friendly delivery together in one platform.
Everything in dataXchange is backed by AdMultaDB. That shared backbone already carries scan rules, schema hints, indexes, transforms, mappings, reporting activity, and cross-workflow state without forcing each team to rebuild the same infrastructure.
Native kernel contracts already target parse, time binning, read counts, and Q3-Q5 reduction paths so high-volume transforms and benchmarks can accelerate the exact hotspots that matter without changing the business workflow around them.
dataXchange sits between operational systems and business outcomes, coordinating ingestion, transformation, analytics, visualization, reporting, and stream processing across AdMultaDB and related delivery programs.
Stream processing is foundational, bringing blocks such as FFT, HannWindow, FrequencyDecimation, PreProcess, ComplexDivide, LoggingReport, and OutputFileStats into the same governed operating surface.
Our platform direction is grounded in real experience working with millions of oil and gas wells and the digital assets around them, covering geology, geophysics, petrophysics, drilling, fracturing, production, and reservoir engineering.
We also bring experience from hundreds of thousands of telecom cell sites and projects across asset management, structural engineering, maintenance planning, and condition-driven construction.
Our delivery experience also includes fiber acquisition data and fiber sensing processing and analysis across hundreds of petabytes of recorded data, where scale, throughput, and clear interpretation all matter at once.
dataXchange extends query processing across the AdMultaDB stack so complex data mining can be delivered through one experience instead of custom tooling for every team.
Natural language access helps analysts and business users start with simple questions, then move into deeper transforms, reports, and model relationships only when they need more precision.
The benchmark experience follows the same Bluesky ndjson workload: point to the files, scan the selected 1, 10, 100, or 1000 tier, build indexes, apply Native acceleration, and run the full Q1-Q5 suite with visible timings and outputs.
Show how a working dataset moves through scan, index, query, acceleration, and leaderboard comparison paths in the same experience that handles intake, projects, and monitoring.