How to Use Data Prep to Accelerate Cloud Data Lake Adoption

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What began as a trickle is now the mainstream: Organizations are moving to the cloud for data management. No longer is the cloud just a cheaper place to park data; it is key to supporting business-critical innovation in advanced analytics, data science, and AI as well as for end-user business intelligence, data exploration, and data visualization. Data lakes and data warehouses running on market-leading platforms such as AWS are growing fast, just as they are on the platforms of competing providers. However, as cloud-based workloads grow in number and size, organizations are facing difficult data preparation challenges. The flow of data raw, diverse, and frequently unstructured can quickly turn cloud data lakes into impenetrable swamps. Without good data preparation technologies and practices, users of all types are frustrated; their productivity and satisfaction suffer because it’s too hard to get accurate data that’s appropriately transformed and structured for their purposes.
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It’s only natural for data analysts or administrators to focus on current needs when setting requirements for new technology. Usability and features of any prospective solution are usually assessed this way. Such a frame is far too narrow. Things change so fast today that the resulting choice will satisfy the demands at hand but is unlikely to meet new demands that arise even in the near future. Suddenly, the once shiny solution has to be replaced, upgraded, handed a crutch. While any number of narrowly focused tools on the market might handle current jobs, best equipped technology to meet today’s and tomorrow’s needs is mature, tested, and solid.

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It’s only natural for data analysts or administrators to focus on current needs when setting requirements for new technology. Usability and features of any prospective solution are usually assessed this way. Such a frame is far too narrow. Things change so fast today that the resulting choice will satisfy the demands at hand but is unlikely to meet new demands that arise even in the near future. Suddenly, the once shiny solution has to be replaced, upgraded, handed a crutch. While any number of narrowly focused tools on the market might handle current jobs, best equipped technology to meet today’s and tomorrow’s needs is mature, tested, and solid.

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