Guide to Data Federation
Data federation lets you access and use data from different places (as if it were all in one database) without moving it.
Data federation lets you view and query data from multiple sources without physically moving or merging it. Because you’re not storing duplicate data, you are avoiding extra storage costs and cutting down on data redundancies. By creating bridges between data sources, such as CRM platforms, databases, and data warehouses, data federation can simplify analysis and decision-making.
This guide explores how data federation works and how it differs from data integration.
How data federation works
Data federation unifies data from various storage systems and programming languages. Its value lies in the ability to present the data as if it were in one place, without copying data.
Core principles
Below are some key principles data federation relies on to give you seamless access to distributed data.
- Data virtualization: Instead of physically moving or replicating data, federation creates a virtual layer (federation layer) to give users a unified view. This approach reduces data duplication and the risk of errors, while avoiding the infrastructure costs of storing redundant data.
- Real-time or near real-time data access: Data federation queries get executed in real time or near real-time, which means you’re getting access to the most updated data rather than waiting for a lengthy extraction and transformation process.
- The federation layer: A single access layer connects all data sources and delivers the results to end users or applications. This means that users interact with the data in a consistent manner, no matter what the underlying source or format looks like.
- Data governance and security: Data federation maintains governance by keeping the original data’s location and any associated controls. It minimizes compliance risks while still allowing for organization-wide access to necessary information.
Architecture
A simplified architecture of data federation can be broken into three distinct layers: data sources, federation layer, and the data consumers.
- Data sources are the data storage systems that federation virtualizes: databases, data warehouses, CRM systems among others.
- The federation layer integrates the data sources to create a unified access framework. It handles tasks such as query distribution and optimization, and ensures the right data is retrieved from the appropriate systems.
- The consumers can be the users or applications that query the data, such as data analysts, architects, or machine learning models.
Query processing
Below are the steps that take place during a federated query.
- Query submission: A user or application sends a request via the federation interface, often using standard query languages such as SQL.
- Federation layer optimization: The federation layer parses (or analyzes and breaks down) the query, identifies the relevant data sources, and optimizes the query for efficiency.
- Data retrieval: Queries are sent to the respective sources, where the requested data is retrieved. The system makes sure only the necessary data is accessed to minimize delays.
- Data “unification”: Results from multiple sources are combined at the federation layer.
- Results: The processed query results are sent back to the consumer, whether it’s a dashboard, report, or application.
Benefits of data federation
Data federation offers plenty of advantages. Below are just a few.
Reduced storage needs
One of the biggest challenges for enterprises today is the sheer volume of data that needs to be gathered, stored, and analyzed so you can make intelligent decisions or arm your AI agents. With data federation you’re not duplicating or copying data into centralized platforms. This means you’re saving on infrastructure costs, reducing the complexities of managing redundant datasets, and minimizing the risks that come with data transfers.
Unified data access
Data federation creates a unified view that eliminates the need to navigate and search disconnected systems, which can slow you down. For example, federation can help marketing and sales teams by giving them a unified view of customer interactions. And IT teams can support the business by adhering to data strategies that eliminate redundancies and maintain data integrity.
Flexibility and efficiency
Because data federation allows you to query data sets in real time, it avoids the delays associated with traditional extract, transform, load (ETL) or extract, load, transform (ELT) processes, where data gets moved and transformed before it can be used.
Federation also streamlines ad-hoc analysis. Data analysts can perform on-the-fly queries, leading to faster decision-making and improved responsiveness to market trends.
Data federation vs. data integration
Data integration usually involves moving and consolidating data into a central repository, often involving ETL or ELT processes. In data federation, data remains in its original source and is accessed on demand.
Another difference is the use of storage. Data federation provides a virtualized view of existing data sources. Data integration sometimes results in duplicate datasets.
Which to use?
If you have to choose between data federation and data integration, first assess your data architecture and your project’s needs.
Use data federation:
- For real-time customer insights across multiple systems
- To perform ad-hoc analysis without complex ETL processes
- When accessing frequently updated data from distributed systems
Example: a financial institution that relies on Oracle to store sales transactions and Salesforce CRM to store customer data can use data federation to create a unified view of all customer interactions.
Use data integration:
- To consolidate data into a single source
- To analyze long-term trends and create forecasts with historical data
Example: a retailer uses MySQL to store inventory data and Salesforce as the CRM. The retailer loads all customer data into a data lake through data integration so they can run queries or use agentic AI.
How Salesforce Data 360 supports data federation
Salesforce Data 360 unifies data and activates it directly in the apps, workflows, and AI agents that power your business. The Data 360 zero-copy data federation allows you to query data from multiple data sources without duplicating data.
Data federation FAQs
Data federation creates a federation layer that lets you view and query data from multiple sources without physically moving or merging it.
Data federation allows you to query and analyze data without having to implement ETL or ELT pipelines. You can analyze trends and make quick decisions to respond to market changes.
Data integration usually involves moving and consolidating data into a central repository. In data federation, data remains in its original source and is accessed on demand.
Ready to take the next step with Data 360?
Talk to an expert.
Tell us a bit more so the right person can reach out faster.
Get started.
Activate Data 360 for your team today.
