With ever more users and uses for data, ad hoc and exploratory analytics have come to the fore. Conventional data warehouses were architected for static, predictable workloads. Snowflake designed for the dynamic requirements of ad hoc analytics.
Conventional data warehouses weren’t built to handle semi-structured data—JSON, Avro, and more. That’s meant additional tools, transformations, and complexity to make it possible to analyze that data. Snowflake was designed with native support for semi-structured and structured data, without compromising flexibility or performance.
A new generation of data-driven applications, delivered as software-as-a-service, offer valuable insights without the complexity and overhead of traditional on-premises solutions. Snowflake’s Elastic Data Warehouse provides the scalable, flexible, and powerful data processing platform that those applications need to process data analytics for their customers.
Organizations are increasingly looking at opportunities to simplify and evolve their data infrastructure by replacing and consolidating legacy data marts and data warehouses. Snowflake provides the opportunity to address challenges with current database options with a solution that takes advantage of existing skills, tools, and processes.