Data Virtuality – A new way to harness the power of your data

Modern data architectures are of the utmost importance in today’s business requirements. Data collection, processing, integration, and execution are the core of successful operations. However, many companies encounter data integration and management challenges while keeping up with the rapidly evolving data landscape. Data Virtuality comes to its clients’ rescue by providing tailored, flexible tools to meet business goals.

With over a decade of experience, Data Virtuality combines innovative solutions with unique products to simplify data-related functions. By enabling data fabric and mesh, Data Virtuality provides self-service capabilities and data governance assistance to enterprises worldwide. Data Virtuality’s tools span multiple industries, such as Financial Services, Retail & E-Commerce, Healthcare, and Telecommunications, and build data structures compatible with modern-day needs.

Leveraging existing data environments through instant data access, data centralization, automation, and governance increase efficiency and performance. Data Virtuality combines two technologies to develop a unique data integration method, the Data Virtuality platform. This medium merges virtualization and next-generation ETL to enable an agile data infrastructure with high throughput.

The Data Virtuality platform supports various solutions that work in tandem to solve the challenges encountered in the data integration space. This product connects to multiple data sources and allows querying data using SQL, whether the data source is relational or non-relational. It also helps with data integration and creates a central data logic that uses SQL to cover the business logic and logical connections between different systems. This platform makes data accessible by supporting all standard interfaces that deliver data to consumers, such as JDBC, ODBC, and REST.

Data Virtuality’s Self-Service Semantic Layer solution enables modern data architectures that democratize data for companies while meeting security, data governance, and data lineage protocols. Using the Data Virtuality Platform, the company allows the building of semantic layers that make data accessible through virtual schemas and views in SQL, regardless of the source. This solution uses a central data access point for all consumers in a controlled environment, making metadata searchable and enabling users to find and work with the data.

In the long run (beyond, say, three years), we will see the continued move away from a monolithic approach to a distributed management of physically decentralized data as it is reflected in the data fabric. There will be no coming back to the monolithic structures of the past,” said Nick Golovin, CEO of Data Virtuality.

The federation engine in the Data Virtuality Platform assists in integration, cleansing, and enriching the data from various sources with the standard SQL, keeping the data accessible and searchable. Its built-in optimization features guarantee the best and most efficient resource utilization. Furthermore, it empowers data consumers to model the data in a virtual context by making it flexible. The platform tests data models, adapts to business goals, and delivers high-quality data.

Data Virtuality offers a metadata catalog feature that allows users to search and download the data, depending on the authorization. With its data lineage abilities, the Data Virtuality Platform immediately displays all data flow-related details, such as the source, queries, and owners.

Finally, the Granular Access Control and Audit tool provides high security, including fine-granular permission layers (schema, table, column- and row level), a built-in user permission system, and versions for all custom metadata. These security features help users to manage sensitive data securely and increase efficiency, allowing them to prioritize relevant data by hiding irrelevant files. Data consumers can always check data access and modification history.

Data Virtuality emphasizes the importance of a Logical Data Warehouse (LDW), a term coined by Gartner. This concept drives businesses to extract value from data. As the company believes in simplified data operations, Data Virtuality’s LDW solution serves as an architectural layer replacing traditional data warehouses and sources. It enables access to multiple, diverse data points through a single access layer that helps users meet every analytical use case.

The Leipzig, Saxony-based company combines data virtualization with automated ETL to enable Logical Data Warehouse architecture. It ensures that users can consolidate all relational and non-relational data sources and use them for immediate analysis using SQL.

As a leading data integration company, Data Virtuality understands clients’ behaviors as part of the data landscape and individual entities and designs customized solutions. Data Virtuality integrates all customer-related data to develop a detailed data-driven marketing sphere. Its well-built customer data integration platform allows customers to centralize the data from all systems and generate touch points. This mechanism helps organizations understand their consumers better and improve their target marketing, customer service, and product planning support. It also increases retention rates, delivers better conversion rates, and lowers customer acquisition costs.

Data Virtuality enhances data mesh by understanding data as the core of internal and external functions. The Data Virtuality Platform oversees preparing and sharing data so that the other domains can utilize it in a self-serving manner. It also comprises automated processes, centralized governance, and security measures to improve the data mesh process. Using this framework, Data Virtuality enables logical and distributed architectures.

Data Fabric is a fundamental data architecture concept that creates a holistic approach to data management. Data Virtuality uses this concept to improve use cases of new and emerging businesses by gaining instant actionable insights using minimal resources. It automates workflows and generates AI-based recommendations to transform the data fabric from a theoretical concept to a practical function.

Data Virtuality has an extensive portfolio of successful user case studies. One of Data Virtuality’s clients, Swanson Health Products, relied on data virtualization and materialization to increase self-service initiatives and reduce insight time. The US brand is a pioneer in providing consumers with affordable, high-quality vitamins and supplements. It uses science-based vitamins and supplements to improve its customers’ physical and mental health.

However, Swanson faced various data management challenges, such as the lack of technology making data inaccessible, increased data latency due to mismanaged data flow, and outdated data collection due to slow data retrieval tools. To help Swanson, Data Virtuality used data centralization and incorporated materialization on several views to centralize the data. Data Virtuality also created connections to available databases and web services to transform and deploy data.

Data Virtuality’s assistance helped Swanson access data by giving authorization to users in the organization. The data integration company also empowered Swanson’s data management team to work efficiently, enabling analysts to deliver timely insights to decision-makers.