Store & Access Information at Scale: How Drawbacks Lead to Innovation

The evolution of storing and accessing information at scale has led to the development of various technologies such as data warehouses, data lakes, and cloud storage. However, these solutions often come with drawbacks like increased time, cost, and complexity. The "schema on write" paradigm, which requires upfront design and build, has become less effective in handling big data analysis. In contrast, the "schema on read" paradigm, which involves streaming data into distributed storage, has its own limitations. A new approach, derived from both paradigms, aims to provide performance and structure without requiring upfront schema and structuring. This is achieved through a unique data format called Chaos Index, which enables in-situ concepts and reduces database resources. By changing how information is stored and accessed, cloud object storage can be transformed into an analytic database, improving overall performance.