Real-time data warehouses are essential for modern applications. Extract-Transform-Load (ETL) as a fundamental component of offline data warehouses also provides crucial support within realtime data warehouses. Among various traditional ETL approaches, Lambda and Kappa have emerged as classic real-time data processing solutions due to their freshness and query performance, which best meet business demands. However, both of them often require the integration of external stream processing engines, introducing challenges related to complexity, efficiency, and consistency. ZeroETL has emerged as an approach to address these issues. Nevertheless, existing ZeroETL-based solutions primarily emphasize the implementation of extraction and loading, resulting in limitations in handling transformation. Incremental View Maintenance (IVM) offers an alternative that can enhance ZeroETL. However, existing IVM implementations often focus on query acceleration rather than supporting high-throughput, complex real-time workloads. To address these challenges, we propose Streaming View, an efficient real-time data processing engine integrated within AnalyticDB of Alibaba Cloud. Unlike existing solutions, Streaming View supports high-throughput, complex data processing for realtime streaming ETL workloads. Furthermore, it can be leveraged to optimize ZeroETL-based approaches by enhancing transformation capabilities. We design tailored algorithms and optimizations for diverse syntaxes and high-throughput scenarios, ensuring the system meets complex application needs. By integrating incremental computation into the data warehouse, Streaming View reduces complexity, ensures data consistency, and boosts performance, offering a robust solution for real-world applications. Experiments show Streaming View improves processing performance by up to 7x and 20x over traditional ETL and IVM methods, respectively, and addresses complex scenarios unsolved by existing solutions.

As an important subsystem that controls the nacelle direction facing toward the inflow wind, the yaw control subsystem plays an indispensable role in wind turbine generation systems. The yaw performance of wind turbines will directly determine the maximum capture capacity of wind energy. However, owing to the lack of effective calibration on wind vane, yaw misalignments are usually present in practice, which highly impacts the performance of yaw control and power generation efficiency. In this study, a data-mining approach on wind vane for yaw misalignment compensation is presented without involving any additional hardware investment. Briefly, the operation data of wind turbines are first evenly divided in terms of measured yaw error, and a power curve is identified for each yaw partition. Quantified generation performance metrics of all power curves are calculated afterwards for yaw misalignment determination, then the yaw control strategy can be finally corrected for wind turbine generation performance improvement. The feasibility and effectiveness of the proposed scheme are testified with simulation and measured data of a 1.5-MW wind turbine.