A Comprehensive Guide to Data Virtualization for Enterprises

A Comprehensive Guide to Data Virtualization for Enterprises

A Comprehensive Guide to Data Virtualization for Enterprises | DeviceDaily.com

 

Enterprises are aggressively investigating beyond the capabilities of traditional data integration such as Extract Transform Load (ETL) systems or data warehouse software as they acquire large volumes of diverse data from an increasing number of sources. Here is a comprehensive guide to data virtualization for enterprises.

Businesses are deploying data virtualization technology solutions to meet increasing data demand for multiple purposes ranging from faster provisioning of new data to enabling self-service data access to clients. It is proving tremendously helpful to data consumers, IT, and technical teams.

Data Virtualization is a Mature Technology

Data virtualization is a mature technology currently used as a part of a company’s data integration strategy. According to MarketsandMarkets, the data virtualization market size is expected to grow to USD 1.58 billion in 2017. Furthermore, it is projected to reach USD 4.12 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 21.1% during the forecast period (2017 to 2022).

Data Virtualization Technology Creates a Logical Extraction Layer

Data virtualization technology creates a logical extraction layer in distributed data management processing. It allows users to access data of any format and heterogeneous source (data warehouse or data lake) in a standardized manner.

As a result, the users of the data do not need to deal with the technical aspects of data, such as where and how the data is stored, the type of data and its storage structure, and the interface of the original source of data storage, etc.

Further, this data is consumed through virtual views by applications, query/reporting tools, message-oriented middleware, or other data management infrastructure components.

How Does Data Virtualization Work for an Enterprise?

Enterprises can easily access the data they require with data virtualization. A three-step process is involved in the implementation of data virtualization:

Connect: Data virtualization connects to varied data sources, i.e., databases, data warehouses, cloud applications, big data repositories, and even Excel files.

Combine: Data virtualization combines and transforms the related information or date of any format into business views or insights.

Deliver: Data virtualization accesses and delivers real-time data through reports, dashboards, portals, mobile apps, and Web applications to enterprises

While data virtualization technology combines various data sources in a single user interface, the virtual or semantic layer is at the heart of the technology. It allows data or business users to organize their data in different virtual schemas further and virtual views in any format and from any source.

Users can access all unified data from diverse systems through the virtual layer, which produces a single consolidated data source. This information is safe and secure and complies with all industry requirements.

Users can easily enhance this virtualized data to prepare it for analytics, reporting, and automation procedures.

Why Do You Need to Virtualize Data?

These factors drive data virtualization’s growing importance:-

Meets data Demands: As enterprises continue to undertake analysis and employ self-service analytics tools, the data demands of business and data analysts, scientists, and engineers on board might become unmanageable. The findings aid businesses in making better decisions and delighting their customers. As a result, data virtualization allows you to view all your data in real-time from a single, centralized location. This enables the completion of analytics faster than usual.

Manages Data complexity and Volume: The quest for fast expansion has increased the number of unconnected physical databases and complex data in businesses. The quickest way to combine them for analytics is to use data virtualization.

The pace of data generation is clearly increasing, making it more challenging to keep a physical data warehouse up to date. In addition, data virtualization is a more advanced method of transferring data from several locations.

Provides Data Agility: While giving business users a self-service option may be a priority, enterprises constantly strive to strike the right balance between strong security and business agility. Data virtualization makes all enterprise data accessible to different users and uses cases through a single virtual layer. In addition, prototyping capabilities are built into data virtualization technology, allowing users to test the strategy in real-time before deploying it on a larger scale.

Provides Secure Governance: As the volume, variety, and complexity of data rises, compliance, data asset protection, and risk mitigation become more critical aspects of every data management strategy.

Data virtualization establishes access rules for who should have access to what data, making the data secure for usage. In addition, it enables security management, data governance, and performance monitoring by providing a centralized point of access to all types of information in the company.

Popular Data Virtualization Tools

Enterprises have been collecting data from numerous destinations or sources into data warehouses, data, or data lakes to consolidate it for further analysis and decision making.

As discussed, with increasing volume and variation of data, the data integration process seems time-consuming, costly, and prone to errors. Thus, many businesses use data virtualization software because it lets them view, access, and analyze data without worrying about the data lifecycle. Here are some popular tools to consider:

TIBCO

TIBCO Software is well-known for its data and analytics software, but it also offers an increasing number of integration options. For example, TIBCO data virtualization allows you to access various data sources. In addition, the tool includes an orchestrated data layer, centralized metadata management, and powerful query options like Advanced Query Engines that aid in data delivery on demand.

The studio design tool, service UI, and business data directory are some of the essential features, which empower users to search for and pick virtualized business data from a self-service directory, and then analyze the findings using their preferred analytics tools. With the help of Web Services Description Language, abstracted data can be made available as a data service in TIBCO. The built-in governance and security ensure that sanctioned data is also delivered to users.

K2View

K2View is a significant figure among vendors in the market. It offers Dynamic Data Virtualization technology for agile data integration by removing the difficulty of accessing data from various underlying data sources, formats, and structures.

Its capabilities range from ingesting data from any source, unifying it via a semantic layer, possibly storing it (physically or in memory), processing it, and eventually making it available to data analysts and consuming applications.

To offer access to real data, this tool uses a logical abstraction layer called data product schema. This schema unifies the information for a specific business entity by bringing together all tables and fields.

It allows you to virtualize or store data with ease. For example, instead of virtualizing data that is not highly dynamic, businesses can choose to keep it. It also allows for smooth data access via any technique, such as SQL or web service APIs, or data delivery (“pushing”) to data consumers via data streaming or messaging protocols.

Denodo

Denodo offers enterprise-grade data virtualization capabilities with an easy-to-use interface. In addition, it includes a data catalog feature that makes data search and discovery easier. This tool can be used on-premises, in the cloud, or a hybrid environment.

Key capabilities include query optimization feature, which improves query performance and reduces response times. In addition, it delivers integrated data governance solutions for enterprises concerned about data protection and compliance.

This tool includes an active data catalog for semantic search and data governance, AI-powered smart query acceleration, automated cloud infrastructure management for multi-cloud and hybrid deployments, and embedded data preparation capabilities for self-service yet well-governed and secure analytics.

Denodo also provides unified enterprise data access, business intelligence, data analytics, and single-view apps.

Conclusion

With the increasing complexity of corporate operations, businesses continue to use various data management solutions. As a result, the data architecture is becoming increasingly intricate.

As a middleware enabling a company to manage data across on-premises, cloud, or hybrid infrastructure, data virtualization is relatively simpler to establish. It enables real-time synchronization of disparate data sources without requiring data replication, lowering infrastructure costs.

Your data engineering team will be able to create clear and concise data views using data virtualization software’s comprehensive analytics, design, and development features. Data virtualization software will enable your data engineering team to design clean and concise data views using rich analytics, design, and development features.

Furthermore, selecting the best data virtualization tool and solution necessitates a thorough examination of their technological capabilities.

The post A Comprehensive Guide to Data Virtualization for Enterprises appeared first on ReadWrite.

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Yash Mehta

Yash Mehta

Yash is an entrepreneur and early-stage investor in emerging tech markets. He has been actively sharing his opinion on cutting-edge technologies like Semantic AI, IoT, Blockchain, and Data Fabric since 2015. Yash’s work appears in various authoritative publications and research platforms globally. Yash Mehta’s work was awarded “one of the most influential works in the connected technology industry,” by Fortune 500. Currently, Yash heads a market intelligence, research and advisory software platform called Expersight. He is co-founder at Esthan and Intellectus SaaS platform.

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