Do you have a data velocity problem?

Data velocity, a critical component of the data landscape, refers to the speed at which data is generated, updated, or deleted and is increasingly challenging traditional data management systems. With the rapid growth in data volume and velocity, existing systems like data warehouses often struggle to maintain performance, causing issues such as decaying query performance and increased computational costs. This challenge is exacerbated by the shift towards real-time data scenarios, which require systems capable of handling high-velocity, semi-structured, or unstructured data. Solutions like open data lakehouses, which integrate technologies such as Apache Iceberg and the Trino open query engine, offer a way to effectively manage high-velocity and real-time data by providing scalable, efficient, and flexible data handling capabilities. These lakehouses enable businesses to work with data at scale more effectively, providing a competitive advantage and facilitating the transition from outdated architectures. Starburst's open data lakehouse solution exemplifies this approach by combining these technologies to enhance data performance and support a range of data workloads, offering features like cluster autoscaling and integration with various data sources, including Hive.