Written by Tamr
Hadoop has helped organizations significantly reduce the cost of data processing by spreading work over clusters built on commodity hardware as well as giving companies the ability to host massive amounts of heterogeneous and diverse data sets. With the growing popularity of Hadoop, a significant amount of organizations have been creating Data Lakes, where they store data derived from structured and unstructured data sources in its raw format. However, these companies struggle with connecting and transforming the data into a unified dataset for business analysis without significant investment in time and money. This is largely because schema proflieration is rampant and very rarely are any two datasets structured exactly alike.
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