What is Data Virtualization?
Data virtualization works as an abstraction layer for the organization’s data assets. This involves aggregating data on-demand from various sources including databases, cloud services and other data repositories. Data virtualization reduces the reliance on traditional ETL (Extract, Transform, Load) procedures by enabling users to retrieve and analyze data in real-time rather than replicating and storing it in a centralized data warehouse. It offers a virtual and unified view of diverse and dispersed data without needing any additional storage.
Features of Data Virtualization
- Centralized security : Centralized security in data virtualization allows data access from a single point, a result of modern data architecture’s efficiency. Every data point can be accessed through the virtual layer and hence, it is possible to apply data security at the row and column level. It is also possible to authorize multiple user groups on the same virtual database by employing confidentiality, anonymization and data masking.
- Flexibility : Data virtualization makes it possible to respond swiftly to new developments in a variety of industries. Comparing this to traditional ETL and data warehousing techniques, it is as much as ten times faster. Data virtualization allows you to respond to new data requests immediately by supplying integrated virtual data objects. This does away with the need to replicate it to different data levels and makes data virtually available.
- Acceleration in time to market from data to completed product : Since virtual data elements incorporate integrated data, they can possibly be built much faster than databases and ETL methods that are currently in use. It is now easier for customers to obtain all the details they need.
- Integrate data from several sources : Heterogeneous data retrieved from data warehouses, data lakes, cloud solutions and machine learning may be easily integrated into data objects required by the end-user thanks to the virtual data layer.
How Data Virtualization Works
Data virtualization involves creating a conceptual layer that masks the physical storage and organization of data from various sources, including databases, cloud services and apps. It serves as an intermediary that dynamically integrates data and gives users and apps a single, virtualized representation of it. Without the requirement for data migration or replication, the data virtualization layer interprets and optimizes requests and queries to retrieve pertinent data from distributed sources in real-time. Organizations can easily access and analyze data with this on-the-fly integration, regardless of its location or format, which fosters agility, simplifies data administration and enables more effective decision-making processes.
Advantages of Data Virtualization
- Real-Time Access : Data virtualization enables organizations to access and query data in real-time from a variety of disparate sources. This real-time access eliminates the need for time-consuming data replication or consolidation processes which provide up-to-date information and insights to decision makers.
- Narrative: The creation of a visualized layer results in minimal duplication and simpler data structures. Additionally, data virtualization reduces the need for complex ETL (Extract, Transform, Load) processes and unnecessary data storage.
- Decreased Redundancy : Elements like graphs, charts, illustrations and images bring the data to life and make it more digestible. The complex data is turned into a memorable piece of information with an effective use of color, design and labeling to ensure clarity and impact. Choosing the right visualization for the data helps engage the right parts of the people’s brains.
- Enhanced Data Governance : With a single virtual data layer fabric, data managers can enforce consolidated data management and security. The information doesn’t need to be moved and accessibility levels can be easily managed. Sensitive data can be suitably safeguarded by implementing security measures at the virtualization layer.
- Cost-effectiveness: Data virtualization allows businesses to optimize resource utilization and save costs along the way. This is done by eliminating the need to store and manage multiple copies of the same data. Costs associated with data infrastructure, storage and maintenance are thus decreased. Moreover, simplified data integration methods improve overall operating efficiency because less resources are required for development and maintenance.
Difference between Data Virtualization vs. Data Federation
|Integrates data from different sources into a single, cohesive view without moving or copying the data.
|Enables the use of dispersed data sources as though they were a single, cohesive source.
|Aggregates and presents data from multiple sources in real-time. This is done by creating a virtual layer that abstracts the underlying data structures.
|Combines data from diverse, distributed sources on-the-fly. It is able to provide a federated view without consolidating the data.
|Graphs, charts, infographics, dashboards and maps
|Data visualizations and storytelling techniques
|Minimizes or eliminates the need to move or replicate data which reduces redundancy and ensures real-time access to the most up-to-date data.
|May involve the movement or replication of data across systems to create a unified view. This might lead to redundancy.
|Provides a consistent and unified view of data. The changes in the underlying sources are reflected in real-time.
|Ensures a federated view, but consistency may be more challenging to maintain.
|Generally, it requires less maintenance as it does not involve management of multiple versions of the data.
|May require more effort to maintain, as changes in source systems may impact the federated view.
|Well-suited for scenarios where real-time access to a unified view of data is critical, such as business intelligence, reporting, and analytics.
|Commonly used in scenarios where a unified view is necessary, but real-time access is not the primary concern.
Data Virtualization Industry Use Cases
- Supply Chain Management : Supply chain management requires streamlining every step of the production and delivery of goods to the final customer. Data virtualization can standardize data from diverse sources, making it easier for suppliers and manufacturers to collaborate seamlessly. It is not easy to obtain the data needed for this from various vendors as well as other business partners. A broader overview of supply chain data can be obtained by integrating data from multiple sources through the use of data virtualization. Both accessibility to data and data engineering productivity can benefit from this.
- Customer 360-degree view : Leveraging data virtualization for a customer 360-degree view enables organizations to get a comprehensive, all-encompassing overview of a customer’s interactions. This covers their past purchases, support requests, engagement on social media and more. Differentiated data from these various locations can be integrated using data virtualization. This gives businesses a unified customer profile that includes details from all data sources.
- Healthcare Industry : A major problem for many firms is complying with regulations. Data collection from many systems, including accounting, HR (Human Resources) and CRM, is frequently necessary to meet compliance standards. Strict regulations governing the healthcare sector need the safekeeping and appropriate handling of patient data. Compliance with regulations can be ensured through the use of data virtualization to combine information from insurance reimbursements, electronic medical records and numerous other inputs into one unified viewpoint.
Getting started with data virtualization
Data virtualization offers numerous benefits such as cost optimization, reduced complexity, flexible access and optimized performance. In order to make the most of these benefits, certain practices must be followed before implementing data virtualization to the enterprise data.
- Concentration – Decisions for data virtualization need to be made by a central authority. This speeds up the process for virtualization of data and databases so moving on to other tasks becomes easier, such as establishing common canonicals and putting in place an automated storage component for real-time data access.
- Shared Data Model – Choose a common data model and put it into practice. By doing so, data reliability and consistency can boost user confidence and make IT professionals more adaptable.
- System of Governance – Guidelines for managing the database virtualization infrastructure should be part of an effective governance model. The governance framework should specify who will oversee shared services and their related infrastructure.
- Establish Advantages – Make sure the appropriate stakeholders are made aware of the advantages. Schedule time for business users to be consulted so that they are aware of the data at their disposal. Make a consistent attempt to persuade other departments that data virtualization is appropriate and necessary.
- Security of data – Although data virtualization renders it simpler to offer a wider range of data to a greater number of users, continued prioritization of data security is essential. For instance, data security policies will assist in identifying the kinds of legislation that may be applicable assuming the data is made accessible to new user categories.
Impact of Data Virtualization using Kyvos Platform
Kyvos is a scalable and high-performance business intelligence and big data analytics solution. By utilizing data virtualization with Kyvos, it can provide real-time access to data from multiple sources. This ensures that the information presented in reports and dashboards is up to date, which allows for timely decision-making. Data virtualization using Kyvos can do away with the requirement for duplicate and redundant data storage. Kyvos allows users to access and analyze data without physically moving or replicating it by building a virtual layer on top of pre-existing sources of data. Kyvos has the ability to create virtual cubes on big data platforms which ensures that users can perform complex, multidimensional analytics without the need to move or duplicate data. As a result, organizations can unlock valuable insights and improve their operational efficiency.Ā« Back to Glossary