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Fulfilling the Promise of Data Lakes:

Data Virtualization Addresses the Challenges of Big Data

Challenges of Handling Big Data

Organizations today can be overwhelmed by the flood of data they must deal with. This data arrives from many sources and also is generated internally – in both instances, rapidly, continuously and in a multiplicity of formats. Big data technology offers a way to cope with this incessant data flow in the form of its capability for the massive storage of data of any type or format along with processing power to transform and analyze the data. Today, organizations often use the open source big data technologies such as Hadoop or NoSQL databases to load raw, detailed data from an array of sources into a consolidated repository called a data lake. Establishing such a data lake enables them to prepare analyses that look across all the data the lake contains.

However, the process of integrating data from multiple sources to enable useful analyses can be challenging for several reasons. One is that organizations generally need to combine information from a large number of sources for optimum access and delivery. According to our benchmark research on information optimization, almost two in five (38%) need to combine more than 10 sources.


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