Description: Federated data refers to a data management approach where information is stored across multiple systems or databases but presented to users as a unified view. This model allows organizations to access and analyze dispersed data without the need to centralize it in a single repository. Data federation is particularly relevant in distributed environments, such as hybrid cloud scenarios, edge computing, and perimeter computing, where data may reside in different locations, including local servers, public and private clouds, or IoT devices. Key features of federated data include interoperability, scalability, and the ability to maintain data privacy and security, as data is not physically moved but accessed in its original location. This approach is essential for organizations looking to maximize their distributed data assets, facilitating informed decision-making and insights generation without compromising data integrity or operational efficiency.
History: The concept of federated data began to take shape in the 1990s when organizations started recognizing the need to integrate data from various sources without the complexity of centralization. With the rise of cloud computing and the proliferation of IoT devices in the 2000s, data federation became even more relevant, allowing companies to manage distributed data efficiently. As data integration technologies evolved, tools and platforms were developed that facilitated the implementation of federated data architectures, enabling organizations to access real-time information from multiple sources.
Uses: Federated data is used in various applications such as real-time data integration, big data analytics, and business intelligence. It allows organizations to access critical information quickly and efficiently without the need to move large volumes of data. It is also useful in regulated environments where data privacy is paramount, as it enables compliance with regulations without compromising access to the necessary information for decision-making.
Examples: An example of federated data usage is in the healthcare sector, where different healthcare providers can share patient information without needing to centralize all data in a single system. Another example is found in the financial sector, where institutions can access customer data from multiple sources to perform risk analysis and regulatory compliance without physically moving the data. Additionally, retail companies use federated data to gain insights into consumer behavior from data scattered across different sales platforms.
