What is data federation? Architecture and use cases

Teams today frequently need access to information spread across multiple disparate systems, so it’s not uncommon to struggle with access and siloing. Getting hold of campaign performance data from Salesforce, ad spend from Google Ads, and revenue numbers from your ERP platform is just as tricky as it sounds.

That’s where data federation comes in, eliminating this issue by offering a unified view of data across different sources without moving it anywhere.

In this guide, we detail what data federation is and how it differs from data warehousing, and share where it fits in a modern data stack through some example use cases.

What is data federation?

Data federation is a data management technique that creates a virtual, unified view of disparate data sources without moving data. Instead of copying data into a centralized repository like a data warehouse, the federation layer sits on top of your existing systems and queries them directly. Disconnected data sources can often lead to silos, but federation solves this issue without requiring a full-scale data integration project. 

Data federation vs. data virtualization vs. data warehousing

People often refer to “data federation” and “data virtualization” interchangeably, but they’re different processes. Virtualization is the umbrella term for any virtual access layer placed over physical data. Federated data architecture specifically connects distinct databases so that businesses can query them as one. 

While data federation keeps everything in place, data warehouses serve as a central repository for data copied or transferred from source systems through ETL or ELT processes. Warehouse content is only as current as the last sync, but since the data has usually been transformed (whether in transit, via ETL, or in its destination, via ELT), heavy analytical queries run much faster. 

For most companies, federation and warehouses work best in tandem: Federation handles real-time operational queries, while warehouses handle heavier analytical work. 

Why is data federation important?

For organizations that need real-time access to information spread across disparate sources, federation is an essential part of the data management process. Here are some of the top benefits:

• Real-time data access: Your analysts can query live production data without having to wait for batch loads. 
• Reduced storage costs: Federation means you don’t need to duplicate data into a second system, reducing your infrastructure load and costs.
• Faster time-to-insight: Instead of having to wait for data transfer and optimization, simple queries return in seconds, meaning less time staring at loading screens and more time spent analyzing.
• Simplified compliance: Sensitive data stays in source systems, governed by any access controls already in place. Federated data governance makes it easier to meet residency and privacy requirements by not having to build a separate permissions layer for any copies.
• Easier source onboarding: Adding a new SaaS tool or database to your federation layer becomes a configuration task rather than a migration project. Your team can start querying the new source alongside existing ones without having to restructure anything.

How does data federation work?

A federated data system operates in three steps:

• Query translation: When a user submits a query, the federation engine decomposes it into sub-queries for each relevant source system. 
• Query processing: The engine then sends those sub-queries out to each source, which processes them independently.
• Data assembly: After collecting the results, the engine reconciles differences in schema and data types and assembles everything into a single response.

This whole process happens in real time. The user only sees one result set, but behind the scenes, the federation layer is coordinating information across your entire tech stack. The layer also maintains each source’s metadata, including schema definitions, data types, and access patterns. It understands how to translate these between systems and can reflect them in query results.

Use cases for data federation

To help you see how data federation could benefit your organization, here are some of the most common use cases.

Business intelligence (BI)

BI teams usually need data engineers to build pipelines before they can use cross-platform dashboards. Data federation removes that dependency. For example, if a BI team uses a federation layer to send queries about CRM, web analytics, and product data, they’d receive a combined result set back in seconds, facilitating ad hoc reporting with no need to wait on anyone.

‍Note: This works well for lightweight, real-time dashboards, though heavier analytical workloads would likely require a data warehouse.

Data science

Creating production models requires data scientists to pull data from a variety of sources, including support logs, product activity, and billing records. Normally, they’d have to submit requests to multiple teams and wait for a response, but data federation provides this information in one central environment, speeding up the early exploration and feature testing phases considerably. 

Regulatory reporting

Compliance teams often need access to consolidated reports that pull information from databases in different countries. Data residency laws can make it illegal to copy this data into a single warehouse. Federation lets you query across regions without data ever leaving its home jurisdiction.

Customer 360 views

To provide the best level of service, sales and support teams want a complete view of each customer, but that information usually lives in three or four different systems. Instead of having to build a complex data warehouse, federation offers a unified view on demand, allowing sales and support reps to pull up a customer’s full history in one query.

Mergers and acquisitions

When two companies merge, their data systems rarely line up, and full migration projects can take the better part of a year. Data federation offers a view of both companies’ systems right away, so sales and finance can operate on shared data while data engineers carry out the longer-term integration in the background.

Challenges of data federation

While data federation can be an integral part of a data management strategy, there are a few drawbacks it’s important to be aware of, such as:

• Performance latency: Federated queries are only as fast as the slowest source. For example, if you’re looking for a combined view of data from a cloud data warehouse and a legacy on-premise Oracle database, the Oracle data would slow everything down.
• Data quality and consistency: Since queries provide a direct view of source system data, if there are any duplicates, nulls, or other inconsistencies, these will also show up in your federated layer. 
• Complex query optimization: Writing efficient queries requires you to understand each system’s performance characteristics. This demands specialized knowledge from your team.
• Governance complexity: Each source system has its own access controls, audit logs, and security policies. Adding proper governance to multiple systems rather than a centralized source is a much tougher job.
• Schema drift: Monitoring and adapting across dozens of sources means your team might have more of an operational burden than initially expected.

Complement your federation strategy with Fivetran

Federated data tools can handle real-time operational queries, but for heavy analytical workloads that drive strategic decisions, you’ll need clean, centralized data.

With over 700 pre-built connectors, Fivetran moves data from your sources into your warehouse automatically, handling schema changes, data aggregation, and incremental updates without any extra effort from your engineers. 

‍Fivetran Transformations cleans and models your data so it’s always analysis-ready. The result is a modern data architecture where federation and warehouse centralization can operate in tandem, each handling the workloads they do best.

To see how Fivetran can transform the way you manage data, demo Transformations or get started with a free trial today.

FAQs

Is data federation better than ETL?

Data federation and ETL solve different problems. Federation provides real-time access to data without you having to move any content, while ETL/ELT centralizes and transforms data for heavy analytics. Most organizations benefit from both processes, deploying federation for operational queries and a data warehouse populated by ETL/ELT pipelines for strategic analysis.

How fast is data federation?

The speed of the data federation process depends on your source systems, your network latency, and the complexity of any queries. Simple lookups across a few sources are fast, while complicated data across multiple systems with millions of rows will be slow.

Can I use data federation with the cloud?

Yes, modern federation tools work across cloud, on-premises, and hybrid environments. Cloud-native federation is becoming more popular as organizations need to query AWS, Azure, and GCP data simultaneously. 

How does data federation support AI and machine learning?

Federation provides data scientists with access to diverse sources for feature engineering and model exploration without having to wait for pipelines to populate data warehouses. But if you’re planning to train large-scale AI models on historical data, a centralized data warehouse or data lake will be more useful.

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