As datasets become larger and more complex, it’s impossible to quickly respond to changing business needs using traditional relational data store such as data warehouse.
Today, most organizations recognize that data is key to the ability to innovate and
remain competitive in a rapidly changing business landscape. A key challenge:
That’s largely because it’s difficult, time-consuming and often expensive to add new
data and access paths to relational data stores. The problem is becoming increasingly
acute as businesses use more unstructured information that relational databases
simply weren’t designed to handle, such as data from Internet of Things devices, the
Web, and social media.
To overcome this challenge, many organizations — including some of the world’s largest companies — are successfully using a proven alternative approach: a data lake. Data lakes support datasets that are extremely large, complex and diverse, and they easily accommodate new data sources such as IoT. They allow IT groups to quickly create new applications that support changing business needs, unlocking the power of complex data for all users within the organization. They also scale much more easily and cost-effectively than relational databases. As a result, data lakes enable greater responsiveness to business groups and external customers, reduced costs, and greater scalability.
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