The performance improvements speak for themselves, as we are seeing up to 10x on common query types (query performance improvement based on internal testing). We are able to use a self-learning AI model to determine the most efficient query execution path. This release marks the completion of phase two for our machine learning (ML) Query Optimizer, and the results are highly encouraging. This seemingly obvious pillar is at the core of ensuring our customer’s success. Security, scalability, and resiliency are part of our DNA.
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