The new wave of data-hungry machine learning (ML) and generative AI (GenAI)-driven operations and security solutions has increased the urgency for companies to adopt new approaches to data storage. These solutions need access to vast amounts of data for model training and observability. However, to be successful, ML pipelines must use data platforms that offer long-term “hot” data storage – where all data is readily accessible for querying and training runs – at cold storage prices.
