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Data Virtualization: What Is It?
Data virtualization is a technology that allows you to access and interact with all your enterprise data in one virtual environment.
The information can be structured or unstructured and stored in a database, data warehouse, data lake, lakehouse, or even in formats like images or emails. Regardless of format or location, and without moving or duplicating, the virtual layer allows you to access, combine, transform, and deliver data.
Since all information stays in its original storage, you save on storage space costs. Additionally, data virtualization is faster than extract/load/transform (ETL) processes. As a result, integrating, delivering, and securing data becomes simpler and more cost-effective.
How Does Data Virtualization Work?
The software for data virtualization acts as middleware that can access all your business data from a single point. It doesn’t care where or how the information is stored.
Since it’s data-source-agnostic, such a platform can be used for a variety of applications. For example:
- Data governance, where it can be used to manage data policies and security
- Business analytics, where you need data from across your organization to make informed decisions
- Single source of truth (SSoT), allowing all stakeholders to see the same, consistent data
Data Virtualization vs Data Federation vs ETL
Data Virtualization vs Data Federation
These two terms are often used interchangeably, but there’s a difference between them. They both help in data management by accessing and combining information from different sources into one place without physically moving it. However, where they vary is how they handle, process, and present it.
Data federation aggregates data by querying each source separately in real time and then combining that information. The data remains in its original format, with little to no transformation. It can be quite resource-intensive, and its performance can be affected when working with large or complex queries across multiple systems.
Data virtualization, on the other hand, is more than just putting information together. It creates a virtual layer that accesses data from multiple sources. However, it also optimizes it and transforms it into a more structured and user-friendly format for presentation. Since it can use cached data and optimized queries, it works better in complex or high-volume situations than federation does.
Data Virtualization vs ETL
ETL is a method of extracting data and transforming it into a unified format. Unlike data virtualization (and federation), it doesn’t just create an aggregated view. It physically copies the required information into a data warehouse or another storage system. While this method is great for batch processing and historical reporting, it can be slow. It also needs additional storage space since it’s copying over data.
Data virtualization, as we already know, doesn’t move data. It just pulls it directly from the source and presents it in an appropriate format.
Key Capabilities of Data Virtualization
Logical Data Abstraction
When you’re looking at information, you just need it to be accurate and relevant. You don’t need to know where it’s stored or in what format. Your data virtualization platform hides these complexities.
It allows you to query and analyze virtualized data from multiple sources as if it were all in one place. In short, it unifies your enterprise data without ever moving or duplicating anything, and allows you to interact with the information without needing to know where it resides.
Real-Time Data Access and Integration
Your data virtualization software connects to the various structured and unstructured data sources within your organization to enable real-time, on-demand access to information. It helps you pull data from all of these sources, and structures it in a format that helps you get the most out of it.
Data Transformation and Optimization
You can transform your data on the fly without ever modifying the original entry. As a result, you can filter, join, and perform calculations across various sources, as needed. Intelligent query optimization techniques make data retrieval quite streamlined as they reduce latency and computational load. Plus, you can cache and index frequently accessed data to reduce repetitive queries and speed up response times.
Security and Governance
Your data virtualization platform can apply consistent access controls across all data sources. It can enforce role-based access control (RBAC), encryption, and authentication to protect sensitive data. It generates audit logs and compliance tracking to monitor who accesses and uses the information, helping you meet regulatory requirements.
Universal Connectivity and API Integration
Data virtualization connects to various data sources and uses SQL, REST, GraphQL, and JSON APIs for easy sharing. This means you can connect your data to BI tools, analytics platforms, and machine learning models. By providing a unified interface for data access, your platform allows your organization to extract insights from your data quite effortlessly.
Data Catalog and Metadata Management
You can easily find, explore, and understand available data through a built-in catalog. AI-driven recommendations and semantic search make data exploration easier, and lineage tracking helps you see where the data came from and how it’s being used.
Agile Development and Self-Service Access
Instead of relying on complex coding or waiting for IT teams to prepare reports, you can access data through a simple, easy-to-use interface. Features like drag-and-drop query building help you filter, sort, and analyze data without requiring technical expertise.
Additionally, pre-built data views and reusable datasets mean you don’t have to start from scratch every time—you can quickly pull up ready-to-use data for faster insights.
The Benefits of Data Virtualization
Remove Silos
Your business collects data from various sources through different departments. In the past, this data would have remained with them. However, modern data aggregation methods, like data virtualization, allow the entire organization to make use of all collected information across departments. Regardless of where the information is stored and by whom, you can access it through your data virtualization platform.
Get Faster Insights and Business Agility
ETL processes take time to compile and deliver relevant data. With data virtualization, you can get the insights you need almost immediately. This helps speed up your decision-making process and makes it more relevant as you’re using real-time data.
Eliminate Duplication and Reduce Costs
Instead of copying data and storing it in a new location every time you make a query, data virtualization simply pulls the data from its original location. As a result, you don’t have multiple copies of data being saved across various locations, which lowers your storage costs. You also reduce the movement of data and redundant ETL pipelines to bring down your infrastructure, maintenance, and licensing costs.
Enhance Security and Governance
We mentioned earlier how data virtualization software helps enforce data security and privacy. As a result, your information is protected from unauthorized access and meets regulatory requirements.
Optimize Scalability and Performance
Data federation requires a lot of resources, while ETL processes take time. However, data virtualization can handle increasing workloads quickly and efficiently. Even if you’re dealing with increasingly high volumes, it won’t affect your platform’s performance.
What Is Data Virtualization Used For?
Real-Time Business Intelligence and Analytics
With data virtualization, you can query and analyze data from multiple sources instantly, ensuring dashboards and reports reflect up-to-the-minute information. This speeds up insights, enhances self-service analytics, and reduces IT dependency.
Applications That Need Real-Time Data
For industries like retail, finance, and logistics, real-time data is critical. Data virtualization helps create a 360° view, merging CRM, sales, and support data for better personalization. It also optimizes supply chains, integrating inventory, shipping, and supplier data. In finance, it detects fraud by analyzing transactions and identifying suspicious patterns in real time.
AI, Machine Learning and Advanced Analytics
AI and machine learning models require high-quality, real-time AI-ready data for better accuracy. Data virtualization ensures seamless access to structured and unstructured information without ETL delays, making it easier to prepare and analyze it for predictive analytics, automation, and financial modeling.
Cloud and Hybrid Data Integration
Managing data across on-premises, multi-cloud, and hybrid environments is complex. Data virtualization unifies access to cloud platforms, data lakes, and legacy systems without costly migrations. This optimizes cloud storage costs while ensuring scalable, flexible access to data.
Compliance, Security and Data Governance
Data security and compliance are critical for businesses handling sensitive information. Data virtualization enables centralized governance, encryption, and RBAC while simplifying compliance with regulations like the GDPR, HIPAA, and SOX.
Data Virtualization With RAW
With RAW, you get more than traditional data virtualization. You also get AI-powered data processing, low-code development, and built-in API management. With Snapi, RAW’s advanced query language, the platform enables real-time data integration across diverse sources without replication.
The platform offers built-in IDE and CI/CD support to streamline development, while secure API hosting ensures seamless data sharing. Designed for AI and analytics, RAW provides businesses with fast, scalable, and intelligent data access.
