How to Use Data Prep to Accelerate Cloud Data Lake Adoption

tdwi

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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Spotlight

Acceleration is an important one that Big Data projects have in common. The V of Velocity turns out to be the most popular of the defining Big Data triad of Volume, Variety and Velocity. Any Variety or Volume focus always leads to the question of how to conduct Big Data Analytics. Velocity however is less about technology and more about the possibilities, about performance and about business impact. Being able to rapidly process and analyze vast amounts of unstructured data is crucial.

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IBM

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Acceleration is an important one that Big Data projects have in common. The V of Velocity turns out to be the most popular of the defining Big Data triad of Volume, Variety and Velocity. Any Variety or Volume focus always leads to the question of how to conduct Big Data Analytics. Velocity however is less about technology and more about the possibilities, about performance and about business impact. Being able to rapidly process and analyze vast amounts of unstructured data is crucial.

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