What We’ve Learned from Over Two Decades of Data Virtualization

In the early 2000s, the enterprise landscape was dominated by Microsoft Office, ERP systems from SAP and Oracle, and CRM platforms such as Salesforce. This dawn of new tech solutions introduced vast amounts of data into business environments, necessitating the creation of new technologies capable of connecting and integrating an increasing amount of information.

Quite quickly, data virtualization emerged as a promising solution. Since the turn of the millennium, we’ve witnessed data virtualization grow in sophistication, utility, and popularity.

Today’s data virtualization tools promise to provide a unified platform that seamlessly integrates all data — whether in the cloud, on-premises, or elsewhere — without data movement or replication. However, despite lofty goals, real-world experiences with data virtualization tools have often fallen short of expectations.

With over two decades of data virtualization behind us, how can we ensure the next era of data virtualization will be even better?

A Brief History of Data Virtualization — And Its Challenges

To predict where data virtualization is heading, it’s helpful to know how far it’s come.

Data virtualization was first created to enable applications to retrieve and manipulate data without needing to know the data’s technical details. Early on, tools primarily focused on on-premises enterprise environments, where they facilitated data publishing from various relational databases like SQL Server and Oracle. While innovative for their time, these early tools were limited in scope and connectivity, and largely reserved for data engineers and IT professionals with strong technical backgrounds.

Throughout the 2000s, major data management platforms embraced data virtualization. As data ecosystems moved from relational databases to data lakes and lakehouses, modern data virtualization platforms evolved to support a range of emerging data sources. This included non-relational databases and cloud storage, as well as a broad spectrum of data management tools for integration, data governance, and other applications.

With enhanced capabilities, modern data virtualization tools help business users access, manipulate, and analyze data directly from various sources without the need for extensive technical knowledge. In an ideal scenario, everyone in an organization has real-time access to enterprise data no matter where it resides. But in reality, most tools fail to achieve this goal in the complex, cloud-native environment.

Businesses struggle to implement data virtualization tools to connect all of the disparate cloud-based data sources, systems, and applications in their tech stack. Despite significant advancements in the functionalities of other cloud-based platforms and applications, many data virtualization vendors have yet to fully embrace cloud-native architectures; some existing solutions struggle to even connect to SaaS applications, cloud storage, and non-relational data sources.

These challenges are holding organizations back from realizing the full potential of their data operations. Non-technical business users are frequently required to go through IT departments to access data, preventing users from leveraging timely data and overburdening IT teams. In fact, 68% of IT workers feel overwhelmed by the number of technical resources required to access data.

Preparing for the Next Era of Data Virtualization

Lessons we’ve learned from the past can help us improve the next generation of data virtualization solutions. For these tools to live up to their full potential, CIOs and other technology leaders play a crucial role in helping our organizations integrate the following components into data infrastructure, tools, and training.

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Connected, Cohesive Data Framework

There’s a clear need for cohesive, large-scale deployments that connect disparate data sources. In fact, eight in 10 business leaders say their organization must prioritize reducing data and information silos.

The diversity of APIs today — from REST to SOAP to protocol buffers — and the myriad data formats like JSON, XML, and CSV, contribute to this fragmented information landscape.

To better unify these sources, we require data virtualization solutions that provide universal, standardized data access and a consistent, well-understood interface across all data environments and consumer applications. Such standardization not only facilitates easier access to data-driven insights, but also supports diverse data workloads and empowers all end users regardless of their technical background.

Cloud-Native Architecture

As organizations accelerate cloud deployments, the scalability and flexibility of the tools at our disposal become even more important. Cloud-native architecture with microservices-based designs is emerging as the answer, enabling efficient data management that quickly adapts to fluctuating demands, without the need for extensive physical infrastructure investments.

The biggest benefit of cloud-native data virtualization platforms is their ability to seamlessly integrate with a multitude of data sources, from traditional relational databases to modern SaaS applications and no-SQL systems. This integration capability ensures your business can leverage all its data assets effectively, facilitating real-time data access and analysis across diverse organizational tools and locations.

Blended Live and Replicated Data

(Valery Brozhinsky/Shutterstock)

A modern data strategy requires a blend of live and replicated data. Your organization benefits from both real-time access for immediate decision-making and batch data movements for comprehensive analytics and historical records, which inform your long-term strategies and ongoing regulatory compliance efforts.

A platform that supports both live data and ETL/ELT workloads across various platforms allows your organization to harness the strengths of both access patterns. This dual approach ensures data is accessible when and where it’s needed most, while always maintaining data integrity and governance practices across different applications, use cases, and users.

Where is Enterprise Data Headed Next?

The history of data virtualization mirrors the broader data revolution — evolving from a rudimentary tool used by a handful of IT professionals to a sophisticated technology facilitating real-time data integration across the entire enterprise.

Today, we’re once again experiencing a profound shift in the way our organizations access, connect, and manage data. With the right data strategy, infrastructure, and technologies, next-generation data virtualization will accelerate our progress and empower all users within an organization to leverage data and drive business strategy and growth.

But remember, the significant progress we’ve experienced over the past two decades didn’t happen on its own. It’s taken leaders and teams to make critical steps to solve long-standing challenges and refine data virtualization tools. Where enterprise data heads next is up to us — and that’s an exciting precipice to stand on.

About the author: As the Founder and CEO of CData Software, Amit Sharma defines the CData technical platform and business strategy. His leadership has guided CData Software’s rise from a startup, to a leading provider of data access and connectivity solutions. Amit holds an MS in Computer Networking from the North Carolina State University and an MBA from the Duke Fuqua School of Business.