The modern data lake architecture combines elements of data lakes and data warehouses to effectively manage large datasets and support advanced AI/ML requirements, adapting to ever-increasing performance needs.
For organizations to successfully adopt AI and machine learning, they require a robust data infrastructure that encompasses not only raw storage but also enhanced compute capabilities necessary for large model training and MLOps.
Data lakes today must be built on modern, performant, Kubernetes-native object storage systems that facilitate efficient streaming, stringent encryption standards, and integration of advanced compute technologies for scalability.
The new architecture allows organizations to leverage both data lakes and Open Table Format specifications, addressing the evolving demands of data management while serving as a foundation for generative AI applications.
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