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A data lake is a centralised repository that can store all of your structured and unstructured data at any scale. Meanwhile, a data fabric is an integrated layer of connected data that is ingested and normalised from an enterprise's data source - regardless of the technology, format, or the whereabouts of the source.
Similar to a weave that is stretched over a large space that connects various locations, types and sources of data; data fabric can process, manage and store the data within the weave. It plays a key role in aligning business goals with the integration, governance, dependability and democratisation of information acquired in huge data lakes.
Depending on your business needs and capabilities, organisations will need to choose the most appropriate big data store for high-scale, real time, and operational use cases. So how can an organisation make this critical decision, and are these two necessarily mutually exclusive?
Learn from our panel of experts as they take a deep dive into the differences between data lakes and data fabric, their respective pros and cons, and whether they can exist simultaneously and complementarity to advance business goals.
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