by Alex Pauncz | June 25, 2021

Data Integration vs. Data Virtualization

Data is the lifeblood of the modern organization, supporting mission-critical enterprise initiatives ranging from artificial intelligence to custom application development and in-depth analytics. The typical large-scale enterprise generates more than a petabyte of new data each year and leverages hundreds of enterprise applications and distinct data sources. As a result, corporate IT is in a never-ending race to keep pace with the data integration needs of their business.

For many, building a connected, streamlined enterprise ecosystem to transform enterprise data into actionable insights is a challenge. Important business data is distributed across a variety of applications and formats, often without any clear linkage between systems.

The goal is typically to deliver a single consolidated system for working with enterprise data instead of managing a complex network of individual data source connections. And for that, most organizations turn to data warehouses.

There are two distinct approaches to data warehousing to consider: physical or logical. The physical data warehouse leverages an integration-based approach. Data is pipelined from various data sources into a single consolidated repository, where that data is transformed and prepared for analytics and reporting. On the other hand, logical data warehousing uses data virtualization to provide real-time access to data through a consolidated interface, without latency or data replication.

This article highlights the important differences between the two and discusses when it's suitable to use data integration vs. data virtualization.

What is Data Integration?

In the process of data integration, you consolidate data from various applications and systems across the organization to enable centralized data access in a database or data warehouse. Your applications, analysts, decision-makers, and developers then query that database to access data, no matter where it's generated.

By consolidating your data from multiple sources into one data warehouse, users and systems can have consistent access to data that meets all their business and application needs.

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 100+ applications and data sources into 30+ databases and warehouses to automate data replication.

Try CData Sync

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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IPaaS Software

iPaaS solutions help organizations integrate applications, processes, and data using connected flows. These platforms layer management and orchestration capabilities on top of APIs, such as drag-and-drop workflow builders, reporting, user management, security, and more.

Modern iPaaS systems enable you to efficiently automate data-sharing across applications for more intricate integrations, and they can often connect to data sources both on-premises and in the cloud.

CData ArcESB is a lightweight, intuitive, and secure iPaaS platform to help you automate integrations, including data flows to and through data warehouses. ArcESB enables you to rapidly complete data connectivity projects in days or weeks, not months.

Explore ArcESB

What is Data Virtualization?

Data virtualization is an entirely distinct process from data integration, as there's no data replication or movement. Rather, the process makes data accessible to other applications and systems without relocating or replicating data.

The data virtualization process effectively creates a virtual layer that allows easy and fast access to live data across applications and platforms.

‌Virtualization saves computing power and storage space because it helps you avoid placing large quantities of data in multiple locations. When there are potentially dozens of sources and hundreds of applications, that adds up to significant savings.

Under the hood, data virtualization consists of a real-time data connectivity layer, an engine for joining data across multiple data sources, and a data discovery and consumption layer. With caching and query optimization also in the technology stack, retrieving data can be highly performant so as not to slow down your application.

With data virtualization, all your data will appear to be in the same database, despite residing at different data sources.

The virtualization technique is perfect for data analytics platforms like Power BI, Tableau, and even Excel - where you don't want to slow down your data processing, need live data, and performance is a priority. Aside from analytics, if you're working with a platform or API with data or limits, data virtualization is a great way to circumvent those limitations.

Embedded Data Virtualization with CData Drivers

CData offers embedded data virtualization technologies, which bring data virtualization capabilities directly into the applications and platforms where you work - without you having to write a line of code.

Our standards-based drivers install easily into any application or tool, including popular analytics and reporting solutions like Power BI, Tableau, and Excel. You can wrangle all your fragmented data painlessly with less time and effort. This is a great advantage vs. engaging in traditional data integrations, months-long migration projects or 7 figure logical data warehouse implementations just to make your data accessible.

This is a huge improvement over having to retrieve entire sets of data from each data source before joining the data and being able to use it on a business intelligence (BI) tool, application, or platform.

We've developed fully integrated connectors for 250+ SaaS applications, CRMs, ERPs, marketing tools, collaboration platforms, accounting solutions, databases, file formats, and other APIs – on-premises or in the cloud.

CData embedded data virtualization technology improves your data access and connectivity without breaking your existing systems and applications.

To learn more about how to overcome data fragmentation challenges the role of embedded data virtualization in data connectivity, watch our free embedded data virtualization webinar.

From the webinar, you'll gain insights into strategies for overcoming data challenges, the latest market trends in data virtualization, and great examples of success in using the technology to solve data fragmentation problems.

Watch the Embedded DV Webinar (On Demand)