A federated database system is a type of meta-database management system, which transparently maps multiple autonomous database systems into a single federated database. The constituent databases are interconnected via a computer network and may be geographically decentralized
As big data platforms become more popular, two models of data management—aggregated and federated—are emerging as two of the most common patterns, but which one is better?
The main difference between the two models is how they store, access, and surface data. The aggregated model (also known as the centralized model) stores data within its system and allows users access to the information in one centralized repository to manipulate the data, normalize it, combine it, and create new insights.
While this model is popular, it doesn’t necessarily meet every organization’s requirements. There can be legal and technical barriers when aggregating data in one centralized location, which is where the federated model becomes attractive.
In contrast, the federated approach surfaces just enough information about the data to tell users where the complete information sits, in the same way that a phonebook will show a person’s name, address, and phone number but not much more. This adds an extra step in retrieving full files such as lab results, but if enough summary information is available, a user can potentially avoid ever using the full file in the first place.
In the case of lab results, the portal that users interact with could surface enough metadata to display key pieces of information—such as the date of a result, type of test, and whether it’s conclusive or not—to avoid having to open the full file. If a user was needing to drill into a result, then they would be able to then go to the original information. The federated model is essentially a well-informed index that provides a path to the original data
What is an example of federation?
An example of a federation is the United States. The act of uniting or of forming a union of states, groups, etc. … A league or association formed by federating, especially a government or political body established through federal union.
What is a federated data platform?
Federating Data is a powerful concept. … Instead, it’s the abstraction of multiple data stores. Then you’ve got the Federated Data Platform, which is basically controlled aggregation to create gold-standard data by using multiple autonomous origin data sources.
What is federated in data warehouse?
A federated data warehouse is the integration of heterogeneous business intelligence systems set to provide analytical capabilities across the different function of an organization.
When would you use a federated database?
Through data abstraction, federated database systems can provide a uniform user interface, enabling users and clients to store and retrieve data in multiple noncontiguous databases with a single query — even if the constituent databases are heterogeneous.
What is Data Warehouse concepts?
A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources.
Aggregated Data Model:
- Lower latency than many federated systems
- High data availability due to a reduced reliance on external systems
- Simpler to normalize the data
- All the data is “at hand” for complex transformations or analytics
- More storage required as the aggregated model stores everything that is required (this can result in added cost and complexity)
- Creates an additional copy of data—potential sync challenges
- Could end up storing more than you should (added cost and complexity), especially if requirements for data are initially uncertain
Federated Data Model:
- Often a lighter weight platform than aggregated
- Avoids creating additional full copies of data
- Easier to introduce new systems/data sources/data fields
- Potential for greater latency—this is especially an issue if the index or summary information is insufficient so that source systems must be tapped the majority of the time
- Potential for lower availability due to dependency on the health of external systems
So, which of the models is better? It’s subjective and really depends on a user’s situation. Each model offers a number of pros and cons, and act as different sides of the same coin. Federated data is lighter weight but can have latency issues, aggregated data takes up a lot of room but is typically faster—each one is similar, but also very different. One model’s pros are the other’s cons.
While there may be different approaches to storing, distributing, and accessing data, there’s no single right way to do it, as each have their benefits and shortfalls. Ultimately, it’s about ensuring secure and timely access to the right information, wherever that data may reside.
The market is starting to demand that large-scale data platforms incorporate the best aspects of both models. To capitalize on this, developers need to incorporate ideas from both models to truly meet the needs of their users both now and in the future.
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