Data Lake vs. Data Hub
Data lakes and data hubs are vastly different at their core. A data lake is designed to store data as efficiently as possible and engineered with legacy technologies like DAS-based storage. The challenge of a data lake is that it creates data silos which inhibit the ability to combine the sets of data needed for analytics into a cohesive whole.
A?data hub?is a modern, data-centric architecture for storage that?powers analytics and AI?by enabling enterprises to consolidate and share data in today¡¯s data-first world. Unlike data lakes and legacy DAS architectures engineered primarily to store data, a data hub is designed to share and deliver data in real time and in a multidimensional way.
Why Data Lakes are Dying
Data lakes are dying because they were built on the obsolete premise that all unstructured data is meant to be stored. Some of it is stored in data warehouses while some is lost in data lakes. The unification of data is broken, and the velocity of data crippled. So why is it so hard for legacy storage systems to unify data on a single platform? The problem is that each application has different requirements for its data¡ªthus, the proliferation of data silos. It¡¯s time to rethink storage.
Data is the fuel for the modern enterprise. Yet, most data is stored in silos, out of reach of analytics and AI applications. Modern intelligence requires an architecture designed not only to store data but also to share and deliver data.
