A guide to data federation: Everything you need to know

Data federation is a data management strategy that can help you improve data quality as well as data accessibility. Data federation is the process of querying data from different sources into a single virtual format.

These federated systems allow for better data integration and analytics than other types of databases because they eliminate the need for massive data storage systems and can provide more accurate information by aligning data across multiple sources. If you’re considering using a federated data model for your business there are a few things you should know.

How does a data federation work?

Data federation doesn’t consolidate information or eliminate other sources. Instead, it leaves the data in the location it already resides while using virtualization techniques to provide a unified view of the information. Essentially, data federation makes it easy to access and find information from various systems as if it is located in one data source.

Data federation also makes it simple to access your information by executing federated queries. These data access points are usually quite easy to use. Even if you employ different data platforms with your data federated solution, end-users who need information from these sources can typically learn how to query their data in a short amount of time. In fact, using data federation can reduce the challenges of analyzing, searching, and finding relevant information in real-time.

Why is data federation so important?

Data federation is one of the most critical data management strategies in today’s data-driven world. With so much data being generated and collected every minute, it’s necessary to have a way to manage all of this information right when you need it.

Businesses across many industries are using data federation for better search results and analytics as well as improved customer interactions. Customers expect companies to be able to provide them with relevant information that pertains specifically to their interests or preferences; businesses can do just that by connecting different sources into one system where they can then use data integration techniques such as de-identification, masking, and anonymization effectively and efficiently. In a federated data model, all of this can be done without creating and storing redundant copies of data.

Data federation helps solve many of the problems that businesses and organizations face when it comes to raw data, whether it has to do with large amounts of data that need storage or a lack of consistency among the data.

Data federation vs. data consolidation

One common misconception that many people have when it comes to data is the assumption that data federation and data consolidation are the same things. However, this couldn’t be further from the truth. Data federation is a data management strategy that can help you connect data from different sources virtually. In contrast, data consolidation involves the process of converting data into one format where it can then be stored in a separate location, often in a data warehouse.

One primary difference between data consolidation (data warehouse) and data federation lies in the fact that once your data is in your data warehouse, there is no need to query any additional systems when looking for information because all of this information is located either in one queriable source. Once your data is there, it can be easy to search through and analyze.

Data federation often allows you to query the most recent data directly from a source system to offer more up-to-date information. This can give your business an advantage when it comes to making timely and accurate decisions. It also allows you to take action based on the data that’s coming in now, which could help you; improve customer interactions, pivot on marketing campaigns, increase or decrease operations spend.

Data warehouses vs. data federation

At a glance, data warehouses are very similar to federated databases because they can both pull data from multiple existing sources to provide information. However, data warehouses require a physical integration, meaning that they store a redundant copy of a dataset so information can be queried directly from it. A federated database on the other hand is virtual and doesn’t physically store any data. Instead, it provides an interface where users can query information across multiple data sources and serve up coordinated information across these non-congruent systems.

Key Concepts of Data Federation

Data federation operates on key principles that make it easier to integrate data from multiple sources without compromising functionality. Here’s what makes it work:

• Autonomy: Each data source maintains full independence, allowing it to keep its original schema, security measures, and management protocols intact. This means no disruption to the way individual systems operate.
• Unified View: Data federation creates the illusion of a single, cohesive system by allowing users to access and query data across diverse sources in one place. This eliminates the need to hop between systems or manually consolidate data.
• Heterogeneity: It supports a wide variety of data formats, database technologies, and access protocols, making it compatible with virtually any platform you use. Whether your data is stored in relational databases, NoSQL systems, or cloud storage, it all works together seamlessly.
• Transparency: The system simplifies access by hiding the complexities of where and how the data is stored. Users don’t need to worry about backend details—they just get the information they need, when they need it, in a way that’s easy to use.

These principles work together to provide a flexible, efficient, and user-friendly approach to managing data from multiple sources.

How Data Federation Works

Data federation simplifies access to diverse data sources by operating through a central hub, known as the federated database management system (FDBMS). Acting as a bridge, the FDBMS allows users to interact with multiple databases as though they were a single, unified source. Let’s break down how this process works step by step:

1. Query Processing: When a user submits a query, the FDBMS analyzes it to determine where the necessary data is located across the connected data sources. This eliminates the need for users to manually search through multiple systems.
2. Query Translation: To ensure compatibility, the FDBMS translates the query into formats that each targeted data source can understand. This step ensures seamless communication between different databases with varying structures and languages.
3. Data Retrieval: Once translated, the system runs the query across the various databases. The relevant data is extracted efficiently, regardless of where it resides.
4. Data Merging: After retrieval, the FDBMS consolidates the results from different sources into a unified, user-friendly format. The final data is then presented to the user, ready for analysis or decision-making.

By leveraging this approach, organizations can access and integrate data from multiple sources without the need to replicate or physically relocate it. This not only enhances efficiency but also provides the flexibility to work with existing infrastructure while ensuring a seamless data experience for users.

Benefits of a federated data model

Data security: There are many data management benefits that data federation brings to the table, including data security. Data federation helps organizations ensure their data is secure by using encryption techniques which make accessing this data difficult for those who don’t have authorization. With these processes in place, it helps to ensure that information is secure while also sharing your data with the right people, like teammates, co-workers, or clients.

One centralized system: Another data management benefit of data federation is that it allows businesses to have one centralized system where data from different sources can be accessed. A federated data model allows you to quickly pull the data you need when you need it.

Accurate data: Data federation takes data management to another level by helping to ensure the data presented is accurate. Businesses can access data from different sources in real-time, which can give them a better idea of what people are thinking or how they feel about their products, services, and more. Instead of relying on out-of-date sources, or one system to provide relevant information, you can search tons of data in seconds to have precise results for your business needs.

Data Accessibility: Data federation enhances data accessibility by allowing users to access and query information from multiple sources without needing to know where it is stored. This means you don’t have to navigate through countless systems or platforms to find what you need. Whether it’s stored on-premises, in a cloud environment, or across multiple databases, data federation simplifies the process, delivering the right information to the right people at the right time. This streamlined accessibility ensures efficiency and supports decision-making by removing unnecessary barriers to data.

Data Integration: Another standout benefit of data federation is its ability to integrate structured and unstructured data seamlessly across different databases. This ensures that no matter how diverse your data sources are, they can work together in harmony. From customer feedback in spreadsheets to system logs in cloud databases, data federation consolidates information without requiring complex migrations or replications. The result is a unified view of your data landscape, enabling businesses to make more informed decisions and leverage all available information efficiently.

Scalability: The scalability of data federation makes it invaluable for growing businesses. Unlike traditional systems that may require extensive and costly migrations when data needs expand, federated systems adapt to increasing demands with ease. As your organization grows and your data sources multiply, data federation ensures that new data can be accessed and integrated without disruption. This flexibility not only saves time and resources but also ensures your data infrastructure remains future-proof, ready to accommodate the evolving needs of your business.

Challenges and Considerations

While data federation offers significant benefits, it also comes with its own set of challenges. Let’s take a closer look at these challenges and what they mean for organizations:

• Maintaining Data Consistency: Ensuring federated data remains accurate and up-to-date across multiple sources can be a complex and ongoing task. With data coming from various systems, there’s always a risk of inconsistencies that can impact decision-making and operations.
• Optimizing Queries: Queries that span across multiple databases or systems can often be slower and require significant fine-tuning to run efficiently. Without proper optimization, these queries can lead to delays and performance bottlenecks, especially for large-scale operations.
• Ensuring Security: Federated systems must handle authentication, authorization, and encryption across different data sources. This introduces an extra layer of complexity, as gaps in security can lead to vulnerabilities, putting sensitive data at risk.
• Managing Implementation: Setting up and maintaining a federated data system requires careful planning, advanced technical expertise, and ongoing management. Without proper implementation, the system may fail to deliver the intended benefits or become overly cumbersome to sustain.

Understanding these challenges is key to successfully implementing data federation and maximizing its potential benefits.

Conclusion

Data management can be challenging, especially when data is scattered across multiple systems that all need to work together. However, federated data models can help simplify data management, and when used correctly, can save businesses time and money. Data storage is extremely costly. Eliminating the need for redundant data copies through a federated data approach is an effective way to reduce data storage costs.

Choosing an industry-leading, federated-friendly business intelligence solution like Domo can help you avoid dataset duplication and unnecessary redundancy. Minimize storage costs, protect your data with encryption best practices, and quickly create datasets with Domo’s federated connectors.