Data federation is the process of using virtualization to have multiple databases function as a unified system to reduce costs and support agility.
One of the biggest technical challenges facing enterprises today is that they have to deal with a constantly growing multitude of applications and data sources. Further complicating matters is the fact that all of these sources have their own data models, constraints, dependencies, and other requirements.
This challenge has resulted in the increasing complexity of data integration workflows. As a result, many enterprises find themselves struggling to overcome operational siloes born of the fact that different departments use different systems that don’t work well together. In the end, sharing information becomes such a burden that each department operates in a bubble.
The rapid migration from on-premises legacy systems to cloud architectures has resulted in yet further complexity. With many businesses having adopted a multi-cloud approach to keep costs low and efficiency high, the sheer disparity of data sources has increased too. Evidently, it’s time to get things back under control.
Managing this complexity requires enterprise technology leaders to rethink the way they work with their data. To become data-driven, they need to consolidate and integrate their various data sources and govern their entire digital infrastructure as a single unit — even if their data remains physically isolated in different systems.
What is data federation, and how is it different from consolidation?
Data consolidation and data federation are sometimes confused, but though they share some important characteristics, they serve quite different roles. Data consolidation is about bringing data together under a single physical or logical environment, such as a data lake, for use in high-speed analytics and other computationally demanding processes.
Data federation follows a different path. Rather than bringing information together, it leaves all data where it is, while providing a unified view of everything through the use of a virtualization layer. This software layer takes all data from all sources and converts everything to a common data model. As such, it offers a way to query and analyze information from multiple, disparate data sources from a single, harmonized dashboard.
What are the main business advantages of data federation?
The main advantage of data federation over data consolidation is that it facilitates real-time access to information, simply because the data is taken directly from its source rather than being put into a data lake beforehand. By contrast, data stored in a data lake is often a day or more old.
Another benefit of data federation is reduced operational risk and cost. Since data federation doesn’t require data to be moved from its original location, there are fewer potential points of failure which, in turn, means reduced risk of data loss. This also means that there is no need for expensive infrastructure or storage upgrade, since data can remain where it already is. A data federation layer is, after all, just a software layer that gives you more control over existing environments.
Data federation also helps organizations maintain digital sovereignty. By contrast, the data consolidation approach requires all data to be logically consolidated into a single environment. That may sound convenient, but it can be problematic if the data in question is subject to very specific compliance demands. Certain compliance mandates, such as those in the healthcare sector, require regulated data to remain within the jurisdiction of the country it is collected in. Data federation supports that degree of flexibility, making it a good workaround in cases where consolidation would either be too costly or come with compliance issues.
Data federation is also ideally suited to low-code environments since the barriers to entry are quite low. For the most part, all IT teams need to do is install data federation software on any standard server, either hosted in the cloud or on-premises, and define the workflows, queries, and views they need. In a low-code environment, citizen developers in each department can do this independently from IT, allowing them to get a granular view of the data-driven insights that matter to them. Moreover, this approach also gives IT teams the ability to implement role-based access controls to govern precisely who has access to which data.
How can an integration platform help businesses with data federation?
Data federation depends on the ability to connect any machine-readable data source used in your organization to a single platform. Although the data itself doesn’t need to be migrated nor modified in place, it does need to be transformed in near real-time into a universal format that allows for unified analytics. In other words, the whole point of data federation is to grant you a big-picture view of your data, from across all sources, while allowing individual departments and end users to home in on the metrics that are important to them.
Integration platforms allow you to connect your data sources to harmonize all your data. With a low-code development solution, you can then build and deploy record-based web apps and customized business workflows on top of that. For example, Corteza transforms all your data streams into a standardized JSON format that’s widely compatible and easy to share and work with. You can then drag and drop visual elements, such as pie charts and graphs, which are automatically kept up to date by the data sources they’re connected to.
That being said, it’s important to remember that there’s no standard definition of precisely what constitutes an integration platform. Some integration platforms offer data federation as a bolt-on extra, while others go so far as to support many different types of data management, such as consolidation, federation, warehousing, virtualization, and application integration.
What are the limitations of data federation?
Most enterprises use a combination of data federation and data consolidation. Both solutions are suited to different use cases, with data federation being ideal when working with regulated data or when real-time (or near real-time) data is required directly from the source.
Data federation certainly isn’t perfect, but it’s the next best thing in any situation where data consolidation is either impractical, prohibitively costly, or where it might result in compliance-related issues. A key limitation of data federation is that it is only possible to apply so much fine-turning and data cleansing across disparate data sources. Large and complex databases, especially those stored in legacy formats on legacy architectures, may still run into problems. That said, an open-source low-code integration platform can provide enterprises the means to overcome such incompatibilities.
In the end, most of the challenges and limitations of data federation can be overcome by using a strong data warehousing and integration platform that provides the opportunity to implement integration workflows of practically any complexity. Doing so may still require the assistance of a third party, but it will still alleviate a great deal of burden from your in-house team.
Planet Crust is the primary contributor to the open-source project Corteza, which serves as a low-code integration platform supporting complex environments. Get started today to see how it works.