One-stop Streaming Data Processing

One-stop Streaming Data Processing Solution Addresses the Challenges Posed by Massive IoT Data

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Data Challenges in IoT Scenarios

High Cost of Data Storage

The sheer volume of data produced by IoT devices is immense, yet the value density of this data is comparatively lower than that of data generated by business systems. This creates a challenging scenario for data storage and management. Effectively storing this data while maintaining fast querying and analysis capabilities without sacrificing performance poses a significant challenge.

Massive High-Frequency Write Load

Modern IoT data collection systems prioritize high-frequency and real-time data collection. The large volume of data writes can present a considerable challenge for traditional databases. Additionally, unforeseen bursts of high-concurrency writes frequently arise as a result of network constraints. These sudden surges in database load impede the system's capacity to deliver uninterrupted service.

Complex Data Architecture

In IoT scenarios, users often encounter the need to employ multiple databases and middleware components to construct complex data processing architectures capable of handling high-frequency writing, real-time analysis and querying, concurrent operations, large-scale storage, and more. Furthermore, managing a substantial number of data flow connections significantly increases the complexity involved in building, operating, and maintaining such systems.

Stringent Real-time Requirements

Many IoT scenarios, such as autonomous driving and smart factories, require real-time data processing and response capabilities. This necessitates data processing systems that can efficiently perform high-speed, low-latency query analysis on massive datasets. Additionally, these systems must be able to conduct real-time computations to meet the demands of instant alerts and decision-making processes.

Solution

MatrixOne's capabilities make it an ideal time series solution for IoT scenarios. It offers high write capacity, efficient data compression, linear scalability, and real-time analysis. With the same hardware configuration, it can handle a larger number of device connections and achieve millisecond-level real-time data collection. As a result, users can enjoy a 10x reduction in storage costs and a 5-10x improvement in performance. MatrixOne also facilitates the integration of production process data, IoT device time series data, data stream transformation, and real-time analysis within a single system. This eliminates the need for multiple databases, reducing the complexity and costs associated with constructing and maintaining IoT data platforms.

One-stop Streaming Data Processing
MatrixOne adopts a cloud-native architecture with completely separated storage, computing, and transaction capabilities, integrating the abilities of OLTP, OLAP, and stream computing into a single database. In IoT time series scenarios, users can accomplish data writing, transformation, storage, and real-time analysis through a single relational database, using the most common SQL syntax, greatly simplifying the data architecture.
High Performance Data Writes
MatrixOne offers support for high-concurrency and high-volume data writes, allowing for dynamic and linear scalability of write capacity to adapt to changes in load. It can handle massive writes while independently scaling high-volume writes without impacting query performance. This is accomplished by configuring compute resource groups to ensure efficient and optimal utilization of compute resources.
Cost-Effective Storage
MatrixOne employs a single storage engine, ensuring that user data has only one storage copy with 50-100% high availability redundancy. This represents a substantial decrease in the number of replicas compared to traditional distributed databases. Furthermore, MatrixOne primarily utilizes a column-oriented data storage format, resulting in compression effects of up to 1%, which significantly lowers storage expenses.
Real-time Data Analysis
MatrixOne features a multi-node parallel computing framework built on the MPP architecture and integrates state-of-the-art vectorized query execution technology. This combination delivers swift performance for various operations including point lookups, batch queries, subqueries, window functions, CTEs, and both simple and complex queries. Additionally, the system ensures linear scalability by scaling across multiple nodes, effectively addressing the real-time analytical requirements of any workload.