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The current scaling approach of Amazon Redshift Serverless increases your compute capacity based on the query queue time and scales down when the queuing reduces on the data warehouse. In this post, we describe how Redshift Serverless utilizes the new AI-driven scaling and optimization capabilities to address common use cases.
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As someone deeply involved in shaping data strategy, governance and analytics for organizations, Im constantly working on everything from defining data vision to building high-performing data teams. My work centers around enabling businesses to leverage data for better decision-making and driving impactful change.
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The difference is in using advanced modeling and data management to make faster scenario planning possible, driven by actionable key performance measures that enable faster, well-informed decision cycles. This may sound like FP&A’s mission today. Today, FP&A organizations perform much of this work manually.
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