Data Virtualization: Benefits, Architecture & Best Practices

Organizations have been able to generate and collect data from different sources, thanks to technological advancements. These data are often scattered and have to be collated. Traditional data integration tools usually struggle to keep up with the speed and complexity of on-demand and real-time analytics. Organizations have turned towards data virtualization to develop a more agile business intelligence system.

Data virtualization techniques provide organizations with a unified view of distributed and disparate data sources without physical movement. In the write-up, we will explore the benefits of data virtualization and how it differs from traditional integration methods. We will also discuss its architectural components, the challenges encountered during implementations, and its best practices.

What is Data Virtualization?

Data virtualization is the modern data integration technology that plays a crucial role in an organization’s business intelligence analytics. It facilitates the extraction of valuable insights by creating an abstracted virtual layer between data consumers and the variety of data sources distributed across different locations or in the cloud. 

The traditional data integration method, like the ETL, involves physically copying data into a general repository, which can cause delay, redundancy, and latency. On the other hand, it reduces data duplication, enables real-time data access, and simplifies integration across multiple resources.

Benefits of Data Virtualization

• Real-Time Data Access: Real-time data access allows users instant access to up–to–date data, enabling timely decisions in companies like healthcare, retail, and finance. It helps business intelligence analysts produce faster results with current information.
• Cost Savings: Data virtualization helps reduce costs by minimizing labor expenses and infrastructure demands, preventing the need for data duplication and storage, and improving resource use by assisting businesses to save time and money.
• Enhanced Decision Making: Data virtualization gives companies a thorough and integral view of their companies, providing a unified view of data, fostering a data-driven culture where teams cooperate freely, and enhancing decision-making accuracy.
• Better Scalability and Flexibility: Data virtualization can enhance business scalability and flexibility by generating a uniform access layer. It permits users to scale resources based on real-time needs, enabling cost efficiency and excellent performance. 

How does Data Virtualization work?

Data virtualization functions by integrating data from multiple sources into a single view without physically moving or duplicating the data. It integrates data to a unified centralized layer creating a duplicate image, allowing users to change data sources without accessing it. It is a middleware that allows data stored in various types of data models to be integrated virtually. The platform allows authorized users to access the company’s entire data from a unified point of access and allows real-time access, and operation without needing technical details about the format or location of the data while keeping the source secure. It assists business objectives by providing a unified source of truth and enabling compliance and data governance. It also complements processes like data quality management, data preparation, data integration, and data warehousing. It also uses layers like data abstraction, data source, transformation, access, metadata, and security to manage and deliver data accurately and effectively.

Data Virtualization vs Data Federation

S/N	Feature	Data Virtualization	Data Federation
1	Definition	Abstract complexities of different data sources	It requires a little planning
2	Best Use Case	straightforward requests on small datasets	Complex requests on large datasets
3	Hardware Requirement	it requires additional hardware but does not store data	Sometimes, it needs more hardware for performance
4	Data Integration	Integrates multiple data forms and sources	Usually restricted to relational data sources
5	Planning Requirement	it is more scalable, supports multiple sources, and is easier to maintain	It requires more planning for a good virtual layer
6	Relationship	Data federation is a feature of data virtualization	It is an Independent data integration method
7	Scalability & Maintenance	It is ideal for simple data sources and scaling can introduce complexities	It is ideal for simple data sources, and scaling can introduce complexities

Architectural Components of Data Virtualization

Data virtualization is a data integration method that enables applications to duplicate and access data without necessarily knowing the technical details. It consists of the following architectural components.

• Consumption Layer: It enables a unified point of access to data through tools and applications like SQL interfaces and Tableau and includes components like data quality control and query optimization.
• Metadata Management Layer: Manages and captures metadata to enable data quality and consistency across multiple sources and governance.
• Query Engine: It handles SQL queries, changing them to processor plans and managing batch processes and memory allocation.
• Abstraction Layer: The abstraction layer acts as a connection between users and data sources, presenting a single logical view of data, enabling data transformation, and concealing the complexity of the underlying data structure.
• Connection Layer: This layer ensures real-time connectors and communication protocols to access data from different sources like data warehouses, cloud services, and databases.
• Data Providers: It involves web services, flat files, and databases that provide the data

Implementing Data Virtualization: Best Practice

1. Centralization of Responsibility: Integrating data virtualization management to simplify processes and improve variables in handling data throughout the organization to enable faster advancement in virtualization efforts and good governance over framework and shared services.
2. Specify a Governance Model: Apply a governance structure that specifies how the environment will be managed. This should specify requirements and the purpose for maintaining shared framework and services, assuring clarity and accountability.
3. Establish a Common Data Model: Conclude on a common data model to ensure high-quality data and consistency to enhance productivity among staff by minimizing complexity in data interpretation and promoting user confidence.
4. Focus on Security: Regular updates and patches are necessary for protecting against exposure that strengthens security measures within the virtual environment by implementing network segmentation, securing hypervisors, and using advanced security features.
5. Performance monitoring: Apply monitoring tools continuously to assess performance system health by utilizing insights gained from upgrade configurations, address any issues emerging quickly, and enhance load balancing.
6. Select the Right Hypervisor: Select a hypervisor that corresponds with your organizational asset and needs and analyze options such as Microsoft Hyper-V or VMware vSphere based on performance, features, and support to allow compatibility with your IT strategy.

Challenges in Data Virtualization

Data virtualization also has its challenges. Below are some of the challenges.

• Limited Manageability: Virtual databases can be difficult to manage, especially with cumbersome datasets that involve meaningful resources for data preparation and management.
• High Initial Costs: It needs careful planning and investment for setting the platform up.
• Limited Scope: Some data sources can be sometimes difficult to virtualize because of compatibility or technical issues.
• Backup Challenges: Platform challenges are usually related to complicated backup, high user expectations, and rapid data growth.
• Performance Issues: The operation is limited to the data sources, and it can be impaired by complicated multiple links and queries, making it less appropriate for real-time applications
• Single Point of Failure: The unified nature of virtualization can lead to a single point of failure affecting other link systems if the server fails.

Data Virtualization Use Cases

Virtualization offers several use cases across different companies and industries.

• Self-Service BI (Business Intelligence): Virtualization helps business users to access and analyze data from multiple sources, improving faster data-driven decisions.
• Real-Time Analytics and Reporting: Virtualization gives real-time instant access to data for generating detailed dashboards and analytics-enhancing business insight.
• Data Integration: It enables ideal data integration from different sources without physically moving data, minimizing dormancy and streamlining complicated integrations.
• Virtual Data Warehouse and Lake: It enables data consolidation and collection from multiple data sources and can be set up faster than a data warehouse. It also allows the management of virtual data platforms by improving data analytics and access.
• Regulatory Compliance: It streamlines compliances by integrating data needed for reporting
• Software Testing and Data Operations: It ensures adequate data management and enables strong data platforms.
• Customer 360–Degree View: It facilitates customer data from various sources to give a complete accurate customer profile. 
• Data Masking and Security: Virtualization uses data-masking rules on virtual data to protect sensitive information and ensure access to data.

Case Study: Successful Data Virtualization Implementation

1. Cisco Case Study

Challenges

• Raising costs for integration and storage of data.
• Data confined in different silos and sources
• Extended progress cycles for making data available to applications.

Solutions

• Used Cisco Unified Computing system and Cisco Application Centric Platform for virtualization
• Applied Cisco Data Virtualization to ensure federated queries across different data technologies.

Benefits

• Quicker application development
• Facilitate data access and visibility for users and business applications
• Minimize data integration and storage costs for IT.

2. Banking Sectors Case Study (Orion) 

challenges

• Complexity in giving single and timely access to real-time information.
• Fragile data systems impact decision-making and efficacy.

Solutions

• Applied data virtualization with Denodo to simplify data access across various environments.
• It allows real-time access without a physical connection.

Benefits 

• Enhanced user experience through single data access.
• Improved operational efficacy and flexibility.

3. Indiana University Case Study

Challenges

• Difficult for decision-makers to access a single view of data.
• Challenges with data silos across multiple systems.

Solution

• Applying virtualization with Denodo to generate logical data warehousing to enable accessing data virtually from various sources.

Benefits 

• Allowed data governance and improved accessibility of data
• Minimize the difficulty of the integration of data.

Interactive Data Virtualization Tools and Demos

• IBM Cloud Pak for Data: It provides AI-driven data virtualization to facilitate data analysis and management.
• Qlik Interactive Data Virtualization: It allows users to analyze and navigate freely with data within virtualizations.
• Red Hat JBoss Data Virtualization: Fit for developer organizations offering virtual data layers across various sources.
• Denodo: It centers on data virtualization with a user-friendly interface, giving demos to show how to create virtual data models from different sources and access them easily.
• Good Data Interactive Visualizations: It allows flexibility in showing data interactively.
• Heavy AI Demos: It provides interactive virtual analysis on broad datasets.
• Visme: It gives interactive data visualizations with hover effects and animations and is good for creating engaging presentations.
•   Zoho Analytics: Zoho Analytics provides interactive virtualizations with robust research capabilities.

Conclusion

In conclusion, data virtualization is a significant technology that allows organizations to analyze and access data from different sources without physically moving or duplicating the data. It provides a single view of data, enabling real-time access and operation, which makes operational and decision-making very effective by simplifying data access and analysis across multiple systems.

It has challenges, such as performance issues and scalability concerns, that need advanced technology and strategic implementation. Virtualization reduces costs, streamlines data retrieval by providing a unified interface for different data sources, provides access to up-to-date data, enhances governance, and facilitates agility to modify business needs. Sign up for Hevo’s 14-day free trial and experience seamless data migration.

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