Exa-d is an internal framework for storing web data in S3, tackling massive scale challenges like heterogeneous content and frequent updates. It uses declarative typed dependencies to manage derived data like embeddings and metadata. This approach avoids brittle scripts, letting engineers define relationships between data columns rather than manual update steps, improving developer efficiency.

Traditional stacks struggled with Exa's need for surgical updates on petabyte-scale datasets. Exa-d allows precise row and column modifications without full rewrites, essential for fixing bugs or rolling out new ML models. By tracking completeness at the fragment level in S3 using Lance, the system minimizes write amplification and supports flexible, dynamic data augmentation.

Execution is optimized for parallel processing across CPU and GPU clusters. The framework automatically determines workflow order based on the dependency graph, skipping cached results to maximize efficiency. This design handles the web's constant churn, ensuring the index stays current. Exa-d represents a custom solution for modern web indexing demands.