Plain, practitioner definitions across real-time, alternative, market, and satellite data - and machine learning.
A working vocabulary for desks and teams that trade and train on data: what each term means, why it matters, and how it shows up in real-time data. New terms get added as we publish.
Real-Time Data
Data delivered the moment it is observed, with minimal delay between an event in the world and its arrival at the systems that act on it.
The delay between an event occurring and the corresponding data being usable - the core quality metric of any real-time dataset.
How recently a dataset was updated relative to the world it describes - closely related to latency, but measured as staleness rather than delay.
A sequence of observations indexed by time - the native shape of prices, sensor readings, and most real-time data.
The documented origin and processing history of a record - where it came from, when it was collected, and how it was transformed.
Alternative Data
Non-traditional datasets - satellite imagery, web activity, card transactions, geolocation - used to form a view before it reaches the mainstream.
Automated collection of publicly available web data at scale - pricing, listings, hiring, reviews - turned into structured datasets.
Anonymized, aggregated card-spend and receipt panels used to estimate a company's revenue before it reports.
Aggregated location signals - foot traffic, dwell time, movement - used as a physical-world proxy for demand and activity.
Estimating the present or very near term - this quarter's revenue, this month's activity - from high-frequency data, before the official number.
Extracting tone or polarity from text - news, filings, social posts - to turn unstructured language into a numeric signal.
Market Data
The most granular market data - every individual trade and quote, timestamped to the microsecond or finer.
The full ladder of resting bids and offers at each price level - market depth beyond the single best quote.
How price and liquidity actually form from the mechanics of orders, spreads, and trading - the physics beneath the tape.
Data that preserves exactly what was known at each moment in history, with no later revisions leaking backward.
The distortion from studying only the entities that survived - dropping delisted and bankrupt names inflates historical results.
Evaluating a strategy or signal against historical data to estimate how it would have performed before risking capital.
The excess return of a strategy above its benchmark - the edge attributable to skill or information rather than market exposure.
Machine Learning
The predictive, actionable information extracted from noisy raw data - what remains after the noise is removed.
A direct, verified observation of the real world, used as the reference against which models and estimates are checked.
The dataset a machine-learning model learns from - and usually the single biggest lever on how well the model performs.
Techniques for turning unstructured text - news, filings, transcripts - into structured, machine-usable data.
Identifying and tagging the real-world entities in text - companies, tickers, people, places - so unstructured content becomes linkable.
Linking records that refer to the same real-world entity across different sources, spellings, and identifiers.
The gradual decay of a model's accuracy as the world shifts away from the data it was trained on.
Data Engineering
The chain of steps that moves data from source to consumer - ingest, transform, and deliver - reliably and on schedule.
An interface that pushes new records to consumers the instant they arrive, rather than making them poll for updates.
A server-to-server callback that pushes data to a URL you register when an event fires - push delivery without a held-open connection.
Reconciling data from many sources into one consistent schema, units, timezone, and set of identifiers.
Removing duplicate and near-duplicate records so each real event is counted once, not many times.
The fitness of a dataset for use, across accuracy, completeness, timeliness, and consistency - for a data vendor, it is the product.
The traceable map of where data came from, how it was transformed, and where it flowed - the backbone of debugging and audit.
Populating a dataset's history - loading past data for a new or changed source - while preserving point-in-time correctness.
Geospatial & Satellite
Overhead images of the Earth captured from orbit, turned into structured measurements of physical activity.
Measuring an object or area without physical contact - typically from satellites or aircraft, across many parts of the spectrum.
Data tied to a location on the Earth's surface, enabling analysis by place and joins across otherwise unrelated sources.
A remote-sensing index that measures vegetation health from red and near-infrared light - a proxy for crop condition and yield.
An active radar imaging technique that sees through cloud and darkness - useful where optical satellites are blind.
Ship-position data from marine transponders, used to track trade flows, port congestion, and commodity movements in near real time.
A live evaluation measures EdgeOrigin against your coverage requirements: decision-ready real-time data, delivery latency into your systems, and record-level provenance.