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.

In the era of big data, the landscape of data management and analytics has significantly transformed, presenting diverse challenges for cloud platforms. Modern data warehouses face increasing challenges in handling hybrid transactional and analytical processing (HTAP) workloads efficiently in cloud environments. Traditional shared-nothing architectures provide high-performance query execution but suffer from high storage costs and limited elasticity, while shared-storage approaches improve scalability but often struggle with query efficiency due to increased data movement and indexing overhead. Furthermore, existing execution engines lack optimized support for vectorized processing and real-time analytics, limiting their ability to handle large-scale workloads efficiently. To address these limitations, we introduce AnalyticDB-PG (ADB-PG), a cloud-native, high-performance data warehouse designed for modern analytical workloads. It integrates a unified architecture supporting both Shared-Nothing and Shared-Storage modes, allowing flexible deployment and seamless elasticity. In ADB-PG, we introduce Beam, a hybrid storage engine that efficiently balances row-based and columnar storage for real-time analytics, and Laser, an optimized execution engine leveraging vectorized execution and Just-In-Time compilation to accelerate query processing. The system further incorporates advanced indexing mechanisms, adaptive runtime filtering, and dictionary encoding to enhance performance. Extensive evaluations on TPC-H and TPC-DS benchmarks demonstrate that ADB-PG achieves significant performance improvements while reducing storage and operational costs, making it a compelling solution for modern cloud-based data analytics.