Hightopo Software partners with Lindorm to pioneer a hyper-converged storage model for industrial IoT (IIoT), serving industries such as manufacturing, smart buildings, and aerospace.
Highlights
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Provides unified storage for hundreds of petabytes of heterogeneous monitoring data from multiple sources.
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Effortlessly handles highly concurrent writes of monitoring metrics with high throughput.
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Enables powerful data visualization use cases with its multi-model data search engine.
Customer testimonial
Lindorm is purpose-built for the demands of industrial IoT data, such as highly concurrent writes and real-time access. It innovatively combines time series, index, and wide table engines into a single, cost-effective solution. This lets us store and analyze high-volume, low-value-density monitoring data with high throughput and low latency, significantly reducing our data storage and O&M costs.
Customer profile
Founded in 2013 and headquartered in Xiamen, Hightopo Software has branches in cities including Beijing, Shanghai, Tianjin, Dalian, and Qingdao. The company specializes in visualization for industrial IoT monitoring and O&M, providing customers with comprehensive services from consulting and design to implementation and after-sales support. With a focus on web-based 2D and 3D graphics component technology, Hightopo is dedicated to making its proprietary HT for Web software a global leader. The HT for Web product series is widely used in industries such as telecommunications, electric power, transportation, water conservancy, petrochemicals, manufacturing, healthcare, and industrial control.
The challenge
The rapid growth of smart technologies like 5G, cloud computing, and edge computing has led to a massive increase in the number and variety of software and hardware sensors in industrial IoT (IIoT) environments. This surge in data volume and diversity makes storage and retrieval increasingly difficult. Traditional solutions often relied on separate, self-managed engines like Elasticsearch for search or OpenTSDB and Prometheus for time series data. However, managing multiple single-model systems to handle diverse data types is technically complex and leads to high O&M costs. The market needed cloud-native storage with multi-model retrieval capabilities. A leading IT consulting firm predicts the IIoT market could reach USD 3.7 trillion by 2025, yet statistics show that less than 30% of vendors are currently profitable, with many falling into a technology trap. While new technologies create opportunities for industrial upgrades, they also introduce new challenges. Complex system architectures and higher demands for performance and stability have become major obstacles to implementing IIoT systems. Enterprises need expert partners to overcome difficulties in building end-to-end data processing platforms, from data collection and transmission to storage, analysis, and visualization.
The solution
Hightopo Software specializes in tackling the "last mile" of the industrial IoT data pipeline: data visualization. The company provides a monitoring visualization solution for IIoT scenarios. Its products enable the rapid creation and deployment of highly customizable and interactive applications, such as network topology diagrams and dashboards. These are ideal for the user interfaces of real-time monitoring systems and are widely used for telecom network topology and device management, as well as in industrial automation (HMI/SCADA) for sectors like power and gas. In scenarios requiring real-time collection of massive data—such as on production lines, at wind farms, or for intelligent transportation situational awareness—the original storage architecture was struggling. It used Elasticsearch, Prometheus, and HBase to separately store time series metrics, logs, user experience data, and network traffic from on-site sensors, third-party systems, and user devices. As data volumes grew and visualization dashboards became more complex, storage and O&M costs skyrocketed, and data retrieval became increasingly difficult, severely limiting the solution's effectiveness.
To address these storage challenges, Hightopo re-architected its storage layer using Lindorm. By adopting a hyper-converged, multi-model approach, Hightopo can store all its monitoring data in a single database (see Figure 2 for a comparison). This dramatically simplifies the storage architecture and lowers O&M costs. Lindorm's proprietary data compression and storage optimization features also deliver significant cost savings for storing large volumes of low-value-density monitoring data. As shown in Figure 3, to handle heterogeneous data from an ever-growing variety of devices, sensors, and third-party systems, a Lindorm of Lindorm integrates multiple engines, including wide table, index, and time series engines. Data is ingested and synchronized between engines using tools like Data Transmission Service (DTS), Data Management (DMS), or open-source ETL software like Apache NiFi and Sqoop, allowing data to be adapted for application needs. For visualization and analysis systems, Lindorm provides easy-to-use SDKs and REST APIs. It is also compatible with native interfaces for OpenTSDB, Prometheus, and HBase, ensuring seamless integration with the mainstream ecosystem and further reducing integration and deployment costs for Hightopo.
Use cases
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Display real-time monitoring dashboards.
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Provide situational awareness and risk monitoring.
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Perform flaw detection using device monitoring data.
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Conduct full historical analysis of fault data.
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Support AI-assisted anomaly detection.
Benefits
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Significantly reduces data storage and aggregation costs in IIoT scenarios with hyper-converged storage for massive, heterogeneous data.
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Keeps data on visualization dashboards current with high-performance, high-throughput data ingestion.
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Ensures business continuity and stability with 99.95% high availability.
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Simplifies network configuration and management with ubiquitous cloud access.
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Reduces system maintenance costs with a maintenance-free, out-of-the-box solution.
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Seamlessly integrates with the mainstream ecosystem through compatibility with OpenTSDB and Prometheus APIs.
Results
Working with Alibaba Cloud, Hightopo supports customers in the Ubiquitous Electric Internet of Things (UEIoT) and smart building industries with a complete cloud-based solution for sensor data collection, storage, and retrieval. The system achieves highly concurrent writes of over 1 million transactions per second (TPS) and can store up to 400,000 time series. This has resulted in a 60% reduction in data storage and system maintenance costs (see Figures 4 and 5).
Figure 4. The monitoring system for the Ubiquitous Electric Internet of Things (UEIoT)
Figure 5. The monitoring system for a smart building use case