Streaming analytics is a continuous processing and analysis of data records in real-time to extract actionable insights and/or generate automated alerts or actions. It's a different approach from batch processing, which involves periodic analysis of large amounts of data aggregated via ETL processes. Streaming analytics tools enable queries on streaming data sources, unlike traditional business intelligence (BI) tools that require static data duplication into data warehouses or proprietary data stores. The technology is used in various industries and use cases, such as transaction analytics, container performance optimization, sensor data analysis, edge computing, and IoT applications. These use cases generate vast streams of operational data that can be processed continuously to extract insights and trigger automated actions. To handle streaming analytics, optimized architectures with low latency queries and ongoing writes are required, which is achieved through column store databases like SingleStoreDB that support fragmented snapshot transactions and optimistic storage reordering.