Years of exponential data growth, evolving business needs, and rising maintenance costs have put a strain on existing data infrastructure. The traditional data warehouse, with its inability to handle data from new sources or handle new innovations such as machine learning or predictive analytics, requires a makeover. What’s needed is not just a new coat of paint or larger data center space – the fundamental architecture needs to change to provide the flexibility organizations need to support current workloads and prepare for future data-driven innovations.Modernizing the data warehouse begins with moving to the cloud. This can involve migrating an existing data warehouse, but a better approach may be starting with a use case that is not well-served by the current infrastructure. “For example, many organizations decide they want to do something with machine learning to reduce their customer churn, so they run a proof-of-concept project,” says Rahul Pathak, Vice President for Analytics at AWS. “That helps them understand how to work with the cloud and how to manage data in the cloud. Then that success leads to more momentum, which may then bring in legacy processes.”To read this article in full, please click her
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