by Danielle Bingham | August 01, 2023

Data Integration vs. Data Virtualization: What’s Best?

As organizations grapple with ever-expanding amounts of data, they need to find ways to manage, integrate, and analyze information stored in one or more cloud services or on-premises—sometimes both—to make sense of it. There are two main approaches to meeting this challenge: data integration and data virtualization.

Both methods integrate data from multiple sources into a unified view—but which one is best?

The answer: It depends.

What is data integration?

Data integration (ETL/ELT) is the more traditional method. Its straightforward approach helps maintain data quality by reducing inconsistencies and errors that might happen during data cleansing and validation. A key component in consolidating and integrating data from different sources, it combines extract, transform, load (ETL) and extract, load, transform (ELT) processes alongside enterprise data warehousing.

The benefits of data integration are well established. It’s a valuable solution for migrating and consolidating data from legacy systems to modern platforms; it’s also scalable and accurate. Data integration is capable of handling massive amounts of data and excels at metadata management for better impact. It answers the need to extract data from external sources, such as APIs, web scraping, and third-party applications, to boost analytical processes.

Data integration methods

Within data integration, you have three primary options to centralize your data: by using application programming interfaces (APIs), integration platforms as a service (IPaaS), or ETL (extract, transform, load).


ETL, or extract, transform, load, is the process of replicating data from across data sources and loading that data into databases and data warehouses for storage. Modern ETL tools also provide ELT, transposing the data loading and transformation steps and leveraging the underlying database to transform the data once it's loaded.

This strategy is popular for handling mass volumes of data and is the traditional approach to data integration. It's ideal for running a wide range of enterprise initiatives, ranging from BI & analytics to AI, app development, and more on top of a central database or data warehouse. By definition, this approach uses pure data integration - integrating your data without integrating your applications.

If you need to manage and automate your data integrations at scale, check out CData Sync, our leading ETL/ELT solution for data integration. With Sync, you can replicate data from hundreds of applications and data sources into any database or warehouse to automate data replication.

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Custom integration and APIs

APIs are the messengers that deliver data between different systems and applications. You can connect your various applications through APIs and run simple API queries to get live data from different sources. You can then use the data to create flexible integrations you can customize with code.

CData simplifies API-based connectivity with a universal API connectivity though the CData API Driver. Built on the same robust SQL engine that powers other CData Drivers, the CData API Driver enables simple codeless query access to APIs through a single client interface.

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What is data virtualization?

Data virtualization transforms data residing in disparate systems into a unified view accessible through a local database or, in the case of CData Connect Cloud, a cloud-native connectivity interface. Robust data virtualization platforms have the capability to virtually access diverse data sources in real-time. This solution enables the publication of organizational data through a single, universally accessible interface.

Unlike traditional data integration approaches, data virtualization retains data in its original systems, employing caching mechanisms that make moving and replicating data unnecessary. The virtualization approach offers agility and flexibility, allowing for easy modifications to data sources or views without interfering with applications. As a result, data virtualization projects have shorter development cycles compared to data consolidation strategies. They can also keep your data more secure, as it is not being duplicated, moved, or accessed by anyone without strict user permissions.

Choosing the best approach

The choice between which integration method to use ultimately depends on the specific requirements of the use case, as well as data volume, complexity, and integration frequency.

Data integration is well-suited for data mining and historical analysis, as it supports long-term performance management and strategic planning. However, it may not be suitable for operational decision support applications, inventory management, or applications requiring intraday data updates. In such cases, data virtualization is preferred over data integration.

Download our whitepaper, Data Integration vs. Data Virtualization: Which is Best?, to learn which approach is right for you.

When to use both data integration and data virtualization

Taking advantage of both methods offers distinct advantages:

Combine and virtualize multiple data warehouses

In data integration, the data source needs to be optimized to ensure compatibility. Adding data virtualization eliminates the need to replicate physical data from the source to provide a unified view.

Modernize legacy systems for historical data analysis

As newer technologies are developed, their compatibility with legacy systems diminishes. Data virtualization used alongside data integration can help create a virtualized view of historical and current data within their modern and legacy storage platforms, making it easy to manage a hybrid cloud data ecosystem.

Augment existing data warehouse infrastructure

Integrating new data sources through ETL/ELT processes expands data warehouse capabilities and allows access to a broader range of information. Data virtualization complements this integration by allowing new sources to be added to the mix with just a few clicks – no custom pipelines needed.

Enable application integration for large datasets

Managing and integrating multiple applications can be a challenge for IT teams. Data integration enables fast data extraction into a storage solution for a cohesive and unified view. Data virtualization can help make sense of the unified data by making it accessible directly within reporting tools for deeper analysis.

Enhance data integration workflows

Data virtualization bridges diverse data sources and integration processes, offering a complete view of applications and systems and removing the need to replicate and move lots of data.

Choose based on objectives

The choice between data integration and data virtualization is based on an organization’s specific needs. Using both in concert with the other helps streamline data management processes, allowing for comprehensive, informed insights to drive business initiatives forward.

To learn more about the differences between data integration and data virtualization, and how both solutions can work together to improve your data strategy, download our whitepaper, Data Integration vs. Data Virtualization: Which is Best?

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CData solutions support both approaches

CData Sync delivers comprehensive support for data integration and transformation processes, and CData Connect Cloud provides next-generation data virtualization for the cloud. These two solutions offer different approaches to both methods and provide flexibility based on specific requirements.

Get started with a free 30-day trial of CData Connect Cloud or CData Sync today.