Today, in the era of big data, IoT and real-time analytics, enterprises need to permanently store an ever-growing volume of structured and unstructured data. According to IDC forecast
, amounts of data stored worldwide will double in the next few years. At the same time, data availability and reliability requirements
are constantly increasing.
It is critical to minimize the risk of a sudden data damage, loss or breach. Data access should be easy, lightning-fast, continuous, and predictable.
While storage infrastructure requirements are constantly evolving, failures can become quite costly for businesses
. Accurate and timely prediction of failures in a storage system enables enterprises to become proactive in preventing data unavailability or loss.
We believe that it is extremely important for TATLIN platform to provide reliable, redundant and highly available storage services. To enable this goal, one of our joint research initiatives focused on finding an effective solution to mitigate TATLIN platform failure and data loss risks by leveraging AI-powered algorithms.
We've partnered with National Research University Higher School of Economics (HSE) and Peter the Great St. Petersburg Polytechnic University (SPbPU) to develop AI-powered algorithms in order to enable predictive storage platform maintenance and health monitoring. As a result of our joint research effort, we have developed an enterprise-ready built-in risk mitigation functionality for TATLIN storage platform that leverages homegrown AI algorithms.