Data Federation: Key Concepts & Best Practices

Managing data spread across several databases and systems is a critical challenge in modern data-driven organizations. Poor data accessibility and integration issues hinder organizations from effectively deriving value from their data. Data federation tools integrate data from disparate sources and create a unified virtual view without duplication. They bring all data into one unified view for improved data management and accessibility. Analytics engines and tools can query numerous databases as one. This article explains the concepts behind data federation, its benefits, and the best practices for implementing data federation. 

Summary of key data federation concepts

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Understanding data federation

Data Federation is a data management approach that aims to provide a unified view of our disparate data sources. It allows users to query and analyze data as if it were stored in the same database without transferring data between databases. The following sections capture key aspects of data federation.

Unified view

The central concept behind data federation is the unified view. All data sources, like on-premise systems, cloud-based databases, or APIs exposed by third-party services, connect to the data federation tool. An abstraction layer in the tool acts as a mediator between users and the underlying data sources. It provides non-intrusive access, fetching data from source systems without altering or duplicating the original data. 

Virtualization

Data federation enables data virtualization. Virtual data representation allows users to interact with underlying data objects as though they were consolidated without physically moving or transforming them. Note that data virtualization and data federation are not interchangeable terms. Data federation combines a series of data stores into one large storage platform. In contrast, data virtualization is a broader term that represents creating an abstraction layer between databases and their users.  Data virtualization can also be done on a single database by abstracting away the technological aspects of the data store.

 Schema translation

A federated data store provides a unified schema by organizing and aggregating the metadata from all the underlying data stores. The abstraction layer translates the schema from every new data source into the unified view, enabling more flexibility. 

In the example above, external tables are mapped to tables within the federated view layer via schema translation. When an external database is onboarded, status_ordersx maps to status.

Benefits of data federation

Data Federation addresses many of the challenges of a traditional data integration system, like ETL and ELT, by providing a unified virtual view of the underlying data source. The following section elaborates on the key benefits of data federation.

Democratizes data access

By providing a single unified view, data federation helps engineers access several data sources without learning the technology stack that powers the underlying databases.  Imagine having a data federation layer over several databases like MSSQL Server, PostgreSQL, and MongoDB. All three of them follow different dialects, with MongoDB having a very different NoSQL structure. Data federation helps in creating a view where engineers can access data from any of these without knowing the underlying technology stack.  

Faster data availability for real-time analytics 

Data federation provides real-time access to data at its source, enabling immediate analytics and AI-driven decision-making. Unlike traditional ETL processes, which are time-consuming and rely on incremental daily updates and transformation steps, this approach reduces latency and bypasses bottlenecks.

Let us have a look at the traditional ETL process example:

The traditional ETL process involves five steps to reach a BI Analyst report from a point-of-sale system. Data federation can bypass this ETL process and reduce the number of steps to 2.

Scalability

As organizations scale, the number and variety of data sources increase significantly. Adding newer data sources can become a tedious process that disturbs the existing system. A scalable integration layer removes friction and helps organizations quickly add data sources to the existing view. 

Improved AI readiness

LLMs require access to correct metadata that can be relied on while feeding context information. For example, when implementing a RAG based on your organization’s data, information about the origin and purpose of data elements, along with the original content in vector databases, is used in the initial shortlisting search. The shortlisted data elements and their metadata are then fed to the LLMs with relevant prompts for the final output. Having federated data and continuous metadata intelligence helps streamline the creation and maintenance of AI-ready data. 

Optimized storage

Data federation combines several data sources backed by varying technology stacks without data duplication. It builds a federation layer over disparate data sources, leveraging the querying layers of underlying data storage systems to provide a single access point and unified view. This mechanism optimizes storage by preventing data duplication. 

Minimize transformation risks

Creating a unified view using alternate architectural patterns like ELT and ETL transforms data from various sources into a form that destination systems can easily consume. It requires executing several complex transformational jobs based on intricate rules and conditions. Designing such a complex web of transformation jobs is error-prone. One can achieve the same result without much risk using a federation layer. 

Data fabric

Federated data opens possibilities for adopting a data fabric architecture within the organization. Data fabric is a holistic data management and integration design concept that relies on loosely coupled reusable data assets across the organization. Beyond having a unified access layer, it focuses on lineage tracking, data governance, and reusable data assets. You can build a data fabric on top of your data federation layer.

Data integration platforms like Nexla align well with a data fabric architecture emphasizing continuous metadata intelligence and universal connectors. Nexla’s core feature is Nexsets, an abstraction over traditional datasets that combines schemas, error management, metadata, access logs, and access controls—all without creating duplicates.

Cost-effectiveness

Data federation helps organizations save money by reducing storage needs and simplifying data pipelines. It is especially valuable for organizations that use multi-cloud setups or a mix of cloud and on-premise infrastructure.

Challenges of data federation

Even though data Federation comes with many advantages, implementing data federation comes with its own unique set of challenges. The following section details the typical challenges involved in implementing data federation.

Handling complex queries

A federated data layer translates user queries to a structure that the underlying data stores understand. It works well for more straightforward use cases primarily involving filtering and data selection. However, data federation does not work well for queries that require complex aggregations over multiple data stores. 

Dependency on underlying data stores

Data federation relies on the ability of the underlying databases to select and filter data according to user queries. While this helps avoid data duplication, it also pressures the underlying database engine. Integrating a transactional database that is already at its maximum capacity can escalate performance issues. 

Data governance concerns

Data federation unifies multiple data stores with independent access controls into a single view. However, this often dilutes data governance policies. Establishing a federated policy without affecting the security of the underlying data stores is a crucial challenge.

Historical change log limitations

Data federation does not effectively implement a historical log of data changes in the underlying stores. ETL architectures can maintain a complete change log by handling the delta data separately. In contrast, data federation only provides the latest view. 

Implementing data federation

Identify requirements

The first step in implementing data federation is to define the objective and catalog all the relevant data sources. One must also consider the federation layer’s users and the kind of queries they are likely to run. Organizations must consider the limitations of data federation while cataloging the data to avoid pitfalls like complex transformations, integrating low-residual-capacity data sources, etc. 

It is also important to define success metrics for quality, performance, governance, security, and accessibility. You can calculate the metrics before and after the implementation to evaluate the effectiveness. 

Establish schema translation

Consistent mapping across schemas from participating databases is the key to realizing the benefits of data federation. The schema must avoid duplication and introduce consistent naming conventions wherever necessary. This step typically requires an elaborate data modeling exercise, considering all underlying data stores. 

Choose a data federation tool

The ideal data federation tool must democratize data access and enable faster data availability without duplicating data. It must be scalable and AI-ready. Consider the following.

Denodo

Denodo’s unified data silos can be incorporated into data platforms for data unification and delivery. These silos are unified data access layers created without physically moving data, providing real-time access to data sources. They can be deployed in AWS, GCP, or Azure cloud accounts, giving users complete control over infrastructure and security.

Nexla

Nexla offers a converged integration approach that combines ETL, API-based integration, and data streaming. It provides a no-code interface for self-service data preparation with automated schema detection and change tracking. Nexla is also strong in quickly implementing AI/ML workflows, including RAGs.

Informatica

Informatica is a data processing platform offering federated data governance capabilities. It provides MDM (Master Data Patterns) and federated architecture options, using data virtualization to provide on-demand integrated views.  

SAP HANA

SAP HANA is a robust real-time analytics and data preprocessing platform that integrates advanced in-memory, column-oriented relational database management systems developed by SAP SE. Robust data federation capabilities are supported via an SDA (Smart Data Access) feature. Smart Data Integration (SDI) can be combined with SDA to allow users to integrate data from remote data sources. 

Deploy securely

Data federations succeed because they allow seamless collaboration between various departments in the organization without compromising data privacy or the security of individual systems. This requires carefully migrating independent data governance policies to a federated governance structure. 

Best practices while implementing data federation

When implementing data federation and virtualization tools into your data platform, it is essential to consider the following:

Establishing metrics

Since the federation layer uses the underlying database’s query layer, monitoring performance metrics provides information about any source system bottlenecks. Define SLAs for consistency, quality, and performance early in the federated system’s implementation cycle. Organizations must continuously monitor consistency and quality metrics to ensure the accuracy of data.

Choosing the right tool

Match tool capabilities with organizational needs (e.g., scalability, ease of use, compatibility with existing systems). The ideal data federation tool must be cost-effective, provide real-time data access, and automate schema translation. Having a no-code/low-code interface to the federation tool helps in reducing time to production and improive acceptance. It should also be scalable enough to facilitate the quick addition of new data sources and be AI-ready. Nexla has all these features and is excellent for implementing data federation and moving toward data fabric design. 

Establishing federated data governance

You must set access control policies to determine who can access data, under what circumstances, and to what extent. Compliance policies ensure adherence to industry standards, regulations, and organizational rules across all data sources. Security policies protect data from unauthorized access and breaches. 

Manage source system changes

A federated layer is a wrapper over several underlying data stores using varying technologies. Any change in source system data models or infrastructure impacts the unified layer. Establishing a repeatable process for managing changes in source systems is key to maintaining the federated layer in good health.  

Conclusion

In summary, data federation efficiently unifies data from multiple sources into one layer, streamlining developers’ access to a data platform. The federated layer allows seamless data retrieval and simplifies the extraction process. As a result, workflows improve, collaboration is enhanced, and we have a more cohesive data ecosystem. The key to success while implementing data federation is choosing the right platform. Nexla is an all-in-one data integration that allows creating virtual data products called Nexsets without consolidating or duplicating them to a single location. You can check out Nexla’s capabilities here.