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Over the years, data lakes on Amazon Simple Storage Service (Amazon S3) have become the default repository for enterprise data and are a common choice for a large set of users who query data for a variety of analytics and machine leaning use cases. Analytics use cases on data lakes are always evolving.
In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. 1: Multi-function analytics . 3: Open Performance.
These inputs reinforced the need of a unified data strategy across the FinOps teams. We decided to build a scalable data management product that is based on the best practices of modern dataarchitecture. Our source system and domain teams were mapped as data producers, and they would have ownership of the datasets.
A modern dataarchitecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale. Metadata table s eliminate slow S3 file listing operations.
Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. A modern dataarchitecture is critical in order to become a data-driven organization.
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