With a Data Fabric both your business users and data scientists can access more reliable and accurate information, with greater agility. This will serve your applications, analytics, machines or systems with Artificial Intelligence, whether in machine learning or business process automation models. All of this helps to make better decisions.
So that technical teams can make use of it to simplify data governance in scenarios configured with complex hybrid and multi-cloud data, what can reduce costs and operational risks significantly.
But why a Data Fabric? What’s is the problem?
a. Constant rise in the number of data sources that need to be integrated and the diversity of these (structured, semi- and unstructured).
b. Increased number of data silos due to a move into more business -owned and business-driven applications.
c. Demand for agility in data integration.
d. Requirements for cost reduction due to increased competition.
e. Migration to the cloud, resulting in hybrid environments where selecting and implementing the best data integration method is key.
So the simpler Data Fabric definition? A catalog of metadata for all data assets that is automatically populated, allows users to traverse how the metadata is related, uses intelligence to add to the metadata, and supports teams who are doing pipeline building (ETL) and anyone who is building and/or using analytics.
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