by Danielle Bingham | August 01, 2023

Data Virtualization vs. Data Integration: Which Is the Best Option for Your Needs?

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.

How data integration works 

Data integration combines data from various sources into a cohesive, unified view. The process typically involves three key steps: Extraction, transformation, and loading (ETL). During extraction, data is collected from different systems, applications. The data is then transformed to match a consistent format, ensuring compatibility and removing discrepancies. Finally, the transformed data is loaded into a centralized repository, such as a data warehouse, where it can be accessed and analyzed. 

Modern ETL tools like CData Sync also support an ELT (extract, load, transform) approach. This method loads raw data into the target system first and then transforms it within the database. This strategy uses the processing power of the database to handle large volumes of data more efficiently. By maintaining the integrity and consistency of data throughout the process, ETL/ELT ensures that the integrated data is reliable and ready for use. 

Centralizing disparate data sources allows organizations to improve data quality, enhance decision-making processes, and support a wide range of initiatives, from business intelligence to artificial intelligence. The integrated data enables comprehensive analysis to support actionable insights, helping organizations stay competitive and agile. 

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/ELT

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 data 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.

Download an API Driver 

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.

How data virtualization works 

Data virtualization creates a virtual data layer that provides a unified view of data from multiple sources without physically moving or replicating the data. This approach uses advanced data abstraction techniques to integrate and present data in real time, enabling users to access and query disparate data sources as if they were a single, cohesive dataset. Data virtualization tools connect to various data sources, such as databases, cloud services, and APIs, and create a virtual representation of the data, which can be accessed through standard query interfaces. 

The process begins with connecting to the original data sources and mapping their data structures into a virtual model. This virtual model abstracts the complexity of the underlying data and presents it in a simplified, consistent format. Caching mechanisms can be employed to enhance performance by storing frequently accessed data temporarily, reducing the need for repeated data retrieval from the source systems. Users can then interact with the virtualized data through familiar tools and interfaces, performing real-time analysis and reporting without the resource-consuming effort of duplicating and moving data. 

Data virtualization offers several advantages, including increased agility and flexibility in data management. It allows for quick integration of new data sources and modifications to existing ones without disrupting applications or workflows. By keeping data in its original location, data virtualization eliminates the security risks associated with data movement and ensures that access permissions and data governance policies are maintained. This approach is particularly beneficial for organizations needing to access and analyze data across a hybrid cloud environment, as it simplifies data integration and enhances overall data accessibility. 

Data virtualization vs. data federation 

Data federation is a technique that aggregates data from different sources into a virtual database, presenting it as a unified dataset without physically moving the data. While both data federation and data virtualization aim to provide a cohesive view of disparate data sources, data virtualization offers more flexibility and real-time access. Data virtualization creates a virtual data layer, allowing for seamless querying and integration without the complexities and performance issues often associated with data federation. This makes data virtualization a more agile and scalable solution for modern data management needs. 

How to choose between data virtualization and data integration 

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 between data virtualization and data integration 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.