CocoIndex is an ETL framework designed to streamline the process of preparing data for AI in real-time. It emphasizes incremental updates, where users specify the transformations and CocoIndex ensures that the derived index remains synchronized with the source data automatically. This is accomplished through effectively identifying changes, recomputing only what is necessary, and leveraging cached results for unchanged data, thus minimizing both computation costs and latency. CocoIndex operates in two modes: batch for one-time updates and live for continuous updates, making it adaptable for various scales of data management.
CocoIndex simplifies the ETL process by enabling users to focus on defining transformations, while it manages data updates efficiently through incremental updates.
With CocoIndex, changes in the source data trigger minimal recomputations, keeping the index updated without large computation costs, making it ideal for low-latency environments.
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