Siglyx provides a low‑code platform for high‑performance processing of ultra‑long time‑series data. It offers pre‑optimized digital‑signal‑processing libraries and visual pipeline building that integrate with existing big‑data ecosystems, reducing the need for custom code and lowering compute costs while maintaining scalability.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations that collect ultra‑long time‑series data (e.g., IoT sensor streams, financial tick data, telemetry) often find that standard big‑data and streaming platforms are not optimized for digital‑signal‑processing (DSP) workloads. This mismatch forces them to build custom pipelines, which are expensive, time‑consuming, and difficult to maintain.
Solution
Siglyx offers a low‑code platform that enables homogeneous, high‑performance processing of massive and continuous time‑series collections. The technology provides pre‑optimized DSP implementations that integrate with existing big‑data stacks, eliminating the need for bespoke code. By abstracting complex signal‑processing tasks into reusable components, the platform reduces development effort and operational costs while preserving scalability. Users can define processing pipelines through visual or declarative interfaces, and the system automatically handles data partitioning, parallel execution, and resource management. The result is faster insight extraction from data that was previously too costly or cumbersome to analyze.
Target Audience
Primary customers are data engineering and analytics teams in sectors such as IoT, telecommunications, finance, and industrial manufacturing that need to process continuous, high‑volume time‑series data efficiently.
Features
- Low‑code pipeline builder with drag‑and‑drop or declarative configuration for time‑series workflows
- Optimized DSP libraries (filtering, Fourier transforms, feature extraction) tuned for ultra‑long data streams
- Seamless integration with common big‑data ecosystems (e.g., Hadoop, Spark, Kafka) via native connectors
- Automatic data partitioning and parallel execution to maximize throughput on commodity clusters
- Cost‑aware execution engine that minimizes compute and storage expenses for large‑scale series
- API and SDK for embedding custom analytics or extending built‑in processing modules