When data is moved to cold storage, it typically becomes dark data – data that is stored and forgotten. But even worse is the outright destruction of data. Often promoted as best practices, these include sampling, summarization, and discarding features (or fields), all of which reduce the data’s value vis-a-vis training ML models.
Current observability, SIEM, and data storage services are critical to modern business operations and justify a significant portion of corporate budgets. An enormous amount of data passes through these new zealand whatsapp number data platforms and is later lost, but there are many use cases where it should be retained for LLM and GenAI projects. However, if the costs of hot data storage aren’t reduced significantly, they will hinder the future development of LLM and GenAI-enabled products. Emerging architectures that separate and decouple storage allow for independent scaling of computing and storage and provide high query performance, which is crucial. These architectures offer performance akin to solid-state drives at prices near those of object storage.
In conclusion, the primary challenge in this transition is not technical but economic. Incumbent vendors of observability, SIEM, and data storage solutions must recognize the financial barriers to their AI product roadmaps and integrate next-generation data storage technologies into their infrastructure. Transforming the economics of big data will help fulfill the potential of AI-driven security and observability.