The current state of the art involves executing data pre-processing, augmentation, and a wide variety of custom ETL workflows on individual client machines. This approach lacks scalability and often results in significant performance degradation due to unnecessary data movement. Unlike most open-source and cloud ETL solutions, AIStore performs transformations on the same machines that store your data, minimizing redundant transfers by exploiting data locality.| AIStore
In distributed systems, maintaining seamless connectivity during lifecycle events is a key challenge. If the cluster’s state changes while read operations are in progress, transient errors might occur. To overcome these brief disruptions, we need an effective, intelligent retry mechanism.| AIStore
The current state of the art involves executing data pre-processing, augmentation, and a wide variety of custom ETL workflows on individual client machines. This approach lacks scalability and often results in significant performance degradation due to unnecessary data movement. Unlike most open-source and cloud ETL solutions, AIStore performs transformations on the same machines that store your data, minimizing redundant transfers by exploiting data locality.| AIStore
In distributed systems, maintaining seamless connectivity during lifecycle events is a key challenge. If the cluster’s state changes while read operations are in progress, transient errors might occur. To overcome these brief disruptions, we need an effective, intelligent retry mechanism.| AIStore