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In today’s business landscape, data insights are crucial for new innovation, informed decision-making, and responding to rapidly changing market conditions. Organizations can benefit greatly with the ability to unify data from across their entire data landscape and make it readily available to everybody who needs it, when they need it. However, data landscapes are constantly evolving as new applications and systems come online, and as migrations to the cloud and next-generation data platforms lead to more data being more distributed across more environments than ever before. Moreover, with the rise of generative AI and intelligent applications, the demand for data is only increasing, challenging data teams as they struggle to keep up.

Traditional approaches to data management rely on physically moving and merging data from a variety of systems into a central repository, such as a data warehouse or a data lake. But this can be both expensive and time-consuming, and the data still needs further preparation for each analytical and operational use case, slowing down the ability of the business to innovate and respond to market changes. A new approach is needed, one that delivers data in the form that each use-case needs, at the speed they need it.

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