From the course: Delivering Data-Driven Decisions with AWS: Applying Machine Learning, Data Engineering, and Generative AI
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Exploring common use cases
From the course: Delivering Data-Driven Decisions with AWS: Applying Machine Learning, Data Engineering, and Generative AI
Exploring common use cases
- [Presenter] Imagine that we are consultants. Let's help our client transform and find the first use case. In this video, we'll learn how to identify use cases, explore industry usage, apply best practices, access Amazon Redshift Serverless as a first time user, and pricing. With our customer's requirements, let's understand their needs. Common use cases include building a data warehouse with just a few clicks using AWS management console for ad hoc analysis. Build a POC to run a complex analytical query, such as materialized views and stored procedures. We can optimize business intelligence tools to refresh reports. With self-service analytics, we can generate insights quickly with query editor v2. Some customers do not want the burden of managing any infrastructure and they need zero administration. If we are unsure of the size of a dataset, Amazon Redshift Serverless could handle analytical workloads of any size and…
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