Data Virtualization

What is Data Virtualization?

Data Virtualization is a data integration approach that allows an application to retrieve and manipulate data without requiring technical details, such as how it is formatted or where it is physically located. It provides a single, unified, and consistent business view of data across various, disparate data sources, making it easier for business users to access data.

Functionality and Features

Data virtualization offers several key features:

  • Real-time access to data: It provides business users with real-time access to data regardless of its location.
  • Data abstraction: It hides the complexities of data, such as its source, format, location, and storage technology, from end-users.
  • Data federation: It aggregates data from multiple sources and delivers a unified, consolidated view of it.
  • Cache: To improve performance, it saves recent or frequent data requests in the cache.
    Data transformation: It transforms data into business-friendly formats.


The architecture of data virtualization comprises of three primary components: the data consumers (applications, BI tools, etc.), the data virtualization layer (which abstracts and provides unified view of the data), and the data providers (databases, web services, flat files, etc.).

Benefits and Use Cases

Among its numerous benefits, data virtualization:

  • Reduces data replication and storage costs
  • Enhances agility due to its capacity for real-time data delivery
  • Supports a diverse range of data formats and types
  • Improves data quality by providing a consistent view of data
  • Simplifies data management and governance

Challenges and Limitations

Despite its advantages, data virtualization also has a few challenges:

  • Latency and performance issues can occur if data is being accessed from multiple, geographically-dispersed sources.
  • Security control implementation can be complex due to diverse data sources.
  • As it depends on source systems for data, any changes in those systems can impact the virtualization layer.

Integration with Data Lakehouse

Implementing Data Virtualization in a data lakehouse environment can simplify data management and enhance accessibility. A lakehouse merges the features of data lakes and data warehouses. Thus, data virtualization becomes a key capability in a lakehouse architecture to provide a unified view of data, regardless of its format or location.

Security Aspects

Data Virtualization employs data security measures like data masking, encryption, and role-based access control to ensure data privacy and compliance with regulations.


While Data Virtualization facilitates real-time access to data, its performance can be influenced by factors such as network latency, the performance of source systems, and hardware limitations.


Is Data Virtualization the same as Data Federation? No, while data federation is a feature of data virtualization, they are not the same. Data federation involves aggregating data from disparate sources, while data virtualization provides an additional abstraction layer, presenting data in a business-friendly manner.

How does Data Virtualization support real-time decision making? Data Virtualization offers real-time access to data from various sources, allowing for instantaneous decision-making based on current data.

What impact does Data Virtualization have on storage costs? By reducing the need for physical data replication, data virtualization can significantly cut down the storage cost.


Data Integration: The process of combining data from different sources into a single, unified view.

Data Abstraction: A process hiding technical details about data, such as its storage location or format.

Data Federation: The process of aggregating data from disparate sources into a unified view.

Cache: A hardware or software component that stores data to serve future requests faster.

Data Lakehouse: A new architecture that combines the benefits of data lakes and data warehouses for analytical and machine learning uses.

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