A decentralized data management approach involves distributing data across multiple systems and locations. This approach allows for greater flexibility and agility, as well as improved data ownership and governance.
The decentralized data management approach has become increasingly popular in recent years, though it comes with its own challenges.
By distributing data across multiple nodes, it reduces the risk of data loss by ensuring that data is stored redundantly across multiple nodes. It also means that if one node fails or is compromised, the data can still be accessed and recovered from other nodes in the network.
By allowing additional nodes to be added to the network when needed, this approach makes it easier to handle large amounts of data and traffic. This can be especially important for organizations that need to process large amounts of data quickly.
Because all nodes in the network have access to the same information, this transparency and accountability can help prevent errors and inconsistencies that can arise when different parties work with different versions of the same data across an organization.
With more data engineers involved in decentralized data solutions, productivity becomes more important, enablement and capabilities become a challenge, and data architecture requires more governance, especially with increasing volume and complexity of data.
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