Real-time Data Warehouse

Real-time Data Warehouse Solution Helps Enterprises Gain Rapid Insights

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Challenges Faced by Enterprises

High Concurrency Results in Write-heavy Workloads

In real-time analysis scenarios, there is a rapid influx of large volumes of data. Traditional database solutions frequently face performance bottlenecks when dealing with such extensive concurrent data streams. These conventional approaches struggle to process and store this data promptly, leading to subsequent data processing stages being limited by the write capacity.

Complex Data Preprocessing Links

Real-time data often demands intricate preprocessing and transformation to fulfill subsequent analytical requirements. These operations need to be performed as the data flows in. Traditional data preprocessing solutions rely on external middleware and necessitate manual creation and upkeep of multiple data preprocessing links. Consequently, these solutions become complex to utilize and manage.

Time-consuming for Complex Queries

Real-time data analysis frequently involves executing complex queries swiftly to provide timely results. Nevertheless, traditional databases often struggle to meet the requirements of real-time analytics due to performance issues, particularly in handling complex queries involving multiple tables.

Poor End-to-end Real-time Performance

The entire process of real-time data analysis encompasses various steps, including data access, preprocessing, storage, analysis, and result feedback. All these steps must be executed in real-time, placing significant demands on the overall performance and efficiency of the data processing system. Traditional data warehouses with multi-tier architectures often struggle to achieve real-time performance at the minute or even second-level for end-to-end processing.

Solution

In data service scenarios like IoT monitoring, fraud detection, and cybersecurity, where vast amounts of data necessitate exceptionally high real-time capabilities, traditional data warehouses often encounter notable performance bottlenecks. MatrixOne, as a hyper-converged cloud-native database, enables straightforward and efficient end-to-end real-time data analysis by offering high-performance data writing, integrated streaming data transformation, advanced analytical query capabilities, and linear scalability.

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.
Built-in Real-time Data Transformation
MatrixOne incorporates a robust streaming computation engine, facilitating seamless real-time data preprocessing and transformation using SQL as the data flows in, to fulfill subsequent analysis requirements. Additionally, it enables real-time synchronization with downstream analytical applications when the upstream original data is updated.
Efficient Complex Query Processing
MatrixOne leverages advanced parallel computing and vectorized execution technology to efficiently handle complex queries over tens of billions of rows of data, thereby enhancing the speed and responsiveness of real-time data analysis. It also supports various common SQL operations such as batch processing, multi-table joins, subqueries, window functions, CTEs, and more, making it user-friendly and accessible for developers.
One-stop Real-time Data Processing
MatrixOne's real-time data warehouse solution encompasses the complete process of real-time data analysis, including data access, preprocessing, storage, analysis, and result feedback, all within a single database, and in a timely manner. This streamlined approach simplifies the data processing workflow, enhances data processing efficiency, and enables true end-to-end real-time data processing.