This project aims to develop a sophisticated real-time data engineering infrastructure to support enhanced analytics for space missions. By leveraging cutting-edge technologies like Apache Kafka, Spark, and Snowflake, we will create an efficient framework to process and analyze vast amounts of data generated during space missions. This infrastructure will enable the organization to make swift, informed decisions, optimize mission outcomes, and ensure data-driven insights are readily available to key stakeholders.
Space mission planners, data scientists, and aerospace engineers who require real-time analytics to optimize mission planning and execution.
Current space mission data processes are hindered by latency and lack cohesion, leading to delayed insights and suboptimal decision-making during critical mission phases.
The space industry faces regulatory pressure to improve mission safety and efficiency, with a clear need for advanced data solutions that provide a competitive edge and compliance with international space standards.
Failure to address these data inefficiencies could result in mission failures, increased costs, and loss of competitive position in the rapidly evolving aerospace sector.
Existing alternatives are limited to batch processing systems that cannot meet the real-time demands of modern space missions, with competitors slowly adopting similar technologies.
The proposed solution offers a unique integration of a data mesh framework with real-time analytics, enabling unparalleled access to mission data and faster decision-making processes.
Our strategy involves partnerships with leading aerospace firms and marketing through industry conferences to demonstrate the value of data-driven space mission enhancements.