Timecho offers an IoT-native time series database built on Apache IoTDB, utilizing a proprietary file format and compression algorithm to achieve over 90% storage cost savings. The platform enables real-time data collection, storage, and analysis for industrial applications, efficiently managing billions of data points while ensuring low latency and high throughput.
Funding
Funding not disclosed
Founders
Product
Problem
Industrial applications generate massive volumes of time-series data from IoT devices, requiring efficient and cost-effective solutions for data collection, storage, and real-time analysis. Existing database solutions often struggle to handle the scale, velocity, and unique characteristics of IoT data, leading to high storage costs and slow query performance.
Solution
Timecho offers an IoT-native time-series database management system built upon Apache IoTDB, designed for edge-to-cloud environments. Its core innovation lies in a proprietary file format (TsFile) and compression algorithm, enabling over 90% storage cost savings compared to traditional databases. The platform facilitates real-time data ingestion, high-throughput writing, and millisecond-level query response, even with billions of data points. Timecho's architecture is optimized for industrial IoT scenarios, supporting various data acquisition protocols, out-of-order data insertion, and one-click data backup.
Target Audience
Timecho targets enterprises in industries such as manufacturing, energy, automotive, and telecommunications that require a scalable and cost-effective solution for managing and analyzing large volumes of time-series data from IoT devices.
Features
- High-compression storage engine based on the TsFile format, reducing storage costs by over 90%
- Distributed architecture enabling second-level scaling without data migration
- Support for hundreds of industrial data acquisition protocols
- Out-of-order data writing to accommodate delayed or unsynchronized data streams
- One-click backup and restore functionality for simplified data management
- Integration with big data ecosystems like Spark, HDFS, and Hive
- High write throughput, capable of ingesting millions of data points per second on a single node
- Millisecond-level query response times for TB-sized datasets