SpaceCurve
Offers a massively parallel data platform for the Internet of Things that ingests, processes and analyzes IoT data in real time.
| Date | Investors | Amount | Round |
|---|---|---|---|
| investor investor | €0.0 | round | |
| N/A | €0.0 | round | |
| investor investor investor | €0.0 | round | |
| investor investor investor investor | €0.0 | round | |
| N/A | €0.0 | round | |
| N/A | €0.0 | round | |
$5.0m | Series B | ||
| Total Funding | 000k | ||
| USD | 2020 | 2021 | 2022 | 2023 |
|---|---|---|---|---|
| Revenues | 0000 | 0000 | 0000 | 0000 |
| EBITDA | 0000 | 0000 | 0000 | 0000 |
| Profit | 0000 | 0000 | 0000 | 0000 |
| EV | 0000 | 0000 | 0000 | 0000 |
| EV / revenue | 00.0x | 00.0x | 00.0x | 00.0x |
| EV / EBITDA | 00.0x | 00.0x | 00.0x | 00.0x |
| R&D budget | 0000 | 0000 | 0000 | 0000 |
Source: Dealroom estimates
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Founded in 2009 by J. Andrew Rogers, SpaceCurve was a Seattle-based company that developed a real-time big data platform. The firm is now out of business. Over its lifespan, the company secured a total of $36.6 million in funding across six rounds from investors including Triage Ventures, REV, and Divergent Ventures. Its final funding was a Series B round that raised $10 million on July 15, 2014.
SpaceCurve's core offering was a database platform engineered to ingest, fuse, and analyze massive volumes of streaming and historical data in real-time. The technology was specifically designed to handle the high-velocity, high-variety data generated by sources like the Internet of Things (IoT), satellites, sensors, and social media. By using space and time as primary indexes, the platform allowed for concurrent data ingestion and complex, ad-hoc queries, providing immediate analytical insights. This was accomplished through a novel architecture that combined CPU sharding with discrete topology internals, enabling massive parallelization of database operations, especially for geospatial and join operations, on commodity Linux clusters.
The platform targeted clients in sectors such as government, defense, transportation, and consumer marketing. Its business model centered on providing this powerful database platform to organizations that needed to make immediate sense of large-scale, continuously generated data from machine sources. For instance, it could enable a telecommunications company to monitor network sensor data in real-time to understand customer movement patterns. The system was built to be exceptionally fast, with the founder noting its ability to insert a billion GeoJSON documents per minute while simultaneously running complex queries.
Keywords: big data platform, real-time analytics, geospatial data, IoT data, sensor data fusion, streaming data, spatiotemporal database, parallel processing, database software, operational intelligence, data ingestion, data fusion, machine-generated data, real-time queries, big data analysis, Andrew Rogers, Seattle startup, venture capital, data infrastructure, defunct company