Industry:

Internet Software & Services

Website

Use case

Electric Brain uses TimescaleDB to build scalable, high-performance data solutions for startups, enabling real-time tracking of lost shipping containers for SeaTrackBox through a combination of time-series and geospatial data management.

Impact

  • Improved data ingestion capabilities
  • Enhanced temporal and geospatial query performance
  • Seamless scalability
  • Simplified cloud infrastructure management
  • Reduced system complexity for startups

Tiger Cloud solution is highly user-friendly, easy to maintain, and can upscale accordingly with the user’s demand.

Thomas Rock, CTO and co-founder

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Company and Use Case
Electric Brain is a French development studio that specializes in backend prototyping, cloud architecture, data, and AI solutions. Their unique offering includes CTO services for equity or royalties, enabling strong client partnerships. One of their standout projects involves designing the data ingestion, storage, and access system for SeaTrackBox, an IoT solution for tracking lost shipping containers. TimescaleDB plays a crucial role in managing the temporal and geospatial data generated by the devices, ensuring performance and scalability in their architecture.

Performance Problems to Solve
Electric Brain often works with early-stage startups that lack the technical resources to develop prototypes or MVPs. In the case of SeaTrackBox, Electric Brain needed a highly scalable and maintainable data solution capable of handling large volumes of temporal and geospatial data. The challenge was to design a cloud infrastructure that could support high-throughput data ingestion while performing complex spatial queries without performance bottlenecks.

Performance Gains Unlocked
By leveraging TimescaleDB's powerful time-series capabilities and PostGIS for geospatial data, Electric Brain created a high-performance architecture that could easily scale with the growing demands of SeaTrackBox. Tiger Cloud made it simple to maintain, ensuring a smooth transition from startup to scale-up. The architecture was capable of handling a high volume of data ingestion through MQTT protocols, while providing efficient and fast access to both temporal and spatial data.

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