In the quest for valuable information, modern big data applications continuously monitor streams of data. These applications demand low latency stream processing even when faced with high volume and velocity of incoming changes and the user’s desire to ask complex queries. In this paper, we study low-latency incremental computation of complex SQL queries in both local and distributed streaming environments. We develop a technique for the efficient incrementalization of queries with nested aggregates for batch updates. We identify the cases in which batch processing can boost the performance of incremental view maintenance but also demonstrate that tuple-at-a-time processing often can achieve better performance in local mode. Batch updates are essential for enabling distributed incremental view maintenance and amortizing the cost of network communication and synchronization. We show how to derive incremental programs optimized for running on large-scale processing platforms. Our implementation of distributed incremental view maintenance can process tens of million of tuples with few-second latency using hundreds of nodes.