The evolution and new era in the space sector is seeing data volumes grow exponentially as sensor platforms provide an unprecedented wealth of information relating to the world and universe in which we reside. Data from sensor technology will generate new ways to garner knowledge and intelligence from multiple terrestrial and space-based telemetric sources in real time. But a major challenge is how to capture and process all that data!

Kx for Space provides a platform for developing solutions ranging from real-time alert management, in-orbit telemetry to health monitoring of systems in operation or under test. Click the link below to read about Kx's participation in NASA's Frontier Development Lab (FDL).

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Kx for Space can ingest vast amounts of historical and real-time information from multiple sources, either in-orbit or from the ground segment, to provide rapid and meaningful insight from analysis of remote sensing data. The underlying technology platform, Kx, provides accompanying tooling and an integrated programming language, q, that enables rapid customization and development of new functionality and analytics.

  • Telemetry

  • Space Situational Awareness

    Space Situational Awareness
  • In-Orbit Processing

    In-Orbit Processing
  • Astronomy and Astrophysics

    Astronomy and Astrophysics


Data from satellites, ground segment and in-orbit may be used to identify faults, predict potential failures and plan maintenance. Sample use cases include:

  • Outlier Detection - Looking for faults within given parameters
  • Alert Management Solutions - Data processing in real time allows immediate investigation and reduced response times
  • Audit and Compliance - Ability to monitor health of fleet and governed by regulations on orbital spectrum
  • Defect Tolerance - Finding mission critical defects in historical data to allow for defect monitoring and ability to perform remote analysis on ground segment / space based assets
  • Historical Data Analysis - Cross comparison and correlation / pattern matching


Areas of application for Kx technology include Space Weather, Satellite Tracking & Monitoring and Near-Earth Objects.

  • Individual Asset: Real-time detection and alert management for changes in orbit, trajectory deviation and signal loss from individual space assets
  • Fleet Level Management: Cross constellation monitoring asset health, failure detection and automates activation of redundancy measures
  • Tracking and analyzing Space Debris data sets for risk mitigation and damage limitation


The amount of quality data gathered remotely by sensors that require harvesting and analysis is growing rapidly, meaning satellite operators require new methods to rapidly ingest, aggregate, inquire, compress and disseminate data from space-borne assets in order to fully exploit this new wealth of information. Kx for Space can be deployed in-orbit to provide a new range of on-board edge analytics and pre-processing solutions for space-borne assets and data.
Click here to read about how Kx's small footprint and high performance make it ideal for in-orbit processing. Kx's low power consumption also makes it ideal for in-orbit and longer duration missions.



The benefits of Kx extend to applications and use cases in astronomy and astrophysics by combining real-time and historical data in analytics.

  • Real-time monitoring of assets with alert capabilities coupled with machine learning for predictive maintenance to drastically reduce costs and improve reliability
  • Outlier detection in both sensor and observational data
  • Historical data analysis and cross-comparison on, for example, command & corresponding control messages beyond specified threshold limits
    To learn more about how Kx can be used in Astronomy, for example running a recessional velocity over 1 million rows of data in a 10th of a second, read our whitepaper.

Or to view a discussion of the paper by the author at recent meetup click the link below

Watch the Video


The kdb+ time-series database, at the core of Kx Technology, is renowned for its computational speed and performance, as well as the simplicity of its architecture for large-scale data analytics.

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