Big data analytics are becoming an inevitable resource for keeping up with increasing competition across global markets and industries. However, enterprise data is often stored in different locations and comes in various forms, such as data lakes, enterprise data warehouses, and specialized databases.
Data virtualization can help businesses to overcome this challenge and release the full potential of their data. This article explains the concepts of data virtualization and how it can help with data management.
What is Data Virtualization?
Data virtualization is an approach to dealing with the challenge of constantly growing amounts of data, it’s complexity and dimensionality. Data virtualization enables analysts, data scientists and APIs to access and query data from different sources without moving or copying it. Most importantly, data virtualization makes data more useful and easier to manage by providing a single virtual layer that spans multiple formats, applications, and physical locations.
Key features of data virtualization include:
- Abstraction — you can access the data without worrying about its location.
- No replication — data is not copied or moved. Instead, it is integrated with other sources, no matter the location.
- Real-time access and data freshness — users can access the data as soon as the source data is changed or updated.
- Agility — by avoiding data modeling and preserving data at its granular form, data consumers can run any query on any data source, whenever needed and quickly adapt to evolving business requirements.
Global Data Virtualization Market and Tools
In November 2018, Gartner estimated that through 2022, 60% of all organizations will implement data virtualization as one key delivery style in their data integration architecture. In its latest survey, Planet Market Reports estimated that the Global Data Virtualization market was USD 1.84 Billion in 2018 and should reach USD 8.39 Billion by 2026, with an average growth of 20.9% from 2019 to 2026.
The main factor driving this market is the generation of large volumes of data and the need for big data virtualization solutions and data integration tools.
The market for big data virtualization tools is highly competitive, with multiple offerings and vendors. These include cloud-based tools like Dremio, Denodo, Varada, AWS Athena, and more. There are several things to consider when choosing a data virtualization tool.
- Performance — data virtualization, which is often based on full scans, can sometimes cause performance issues that lead to slow queries and high resource consumption. Many vendors try to optimize query performance to enable more near real-time queries.
- Applications — make sure that the data virtualization solution integrates with your most demanding applications and is strongly aligned with business requirements.
- Compliance and security — choose a solution that addresses the existing security and compliance requirements of data virtualization.
Data Virtualization Use Cases
The following use cases show how data virtualization can help businesses address the main challenges of data management.
Improves big data analytics
Big data analytics examines large volumes of data to discover hidden correlations, patterns, and other insights. Organizations leverage big data analytics to identify new business opportunities or improve operations. However, due to the increasing complexity and volume of data, companies must adopt advanced technologies like data warehouses, Hadoop, and real-time analytics solutions to benefit from emerging opportunities.
Data virtualization enables one to create logical views of data from different sources. This abstraction of data makes the data available for analytics much quicker. Additionally, it allows for easy integration with business intelligence tools, data warehouses, and other analytics platforms.
Enable big data analytics directly on the data lake
One of the advantages of data virtualization is that it enables data consumers to be “agnostic” to the data source. Essentially, they don’t need to know or care where the data resides in order to query it effectively. As the data lake architecture gains momentum in organizations, due to the agility and cost efficiencies it delivers, data virtualization layers can speed up the adoption of data lakes across a wide variety of data-driven applications.
Improves logical data warehouse functionality
Virtualization in a logical data warehouse architecture enables one to gather analytical data in a single logical place, regardless of the application or source. Virtualization allows quick data transfer through several commonly used APIs and protocols, such as JDBC, REST, and ODBC. It ensures compliance with the requirements of a Service-Level Agreement (SLA) by enabling workloads to be automatically assigned.
Optimizes the enterprise data warehouse
Organizations use data warehouses to handle massive amounts of incoming data from multiple sources and prepare it for query and analysis. However, traditional data integration techniques like ETL are suitable for bulk data movement. As a result, one must work with outdated data from the last ETL operation. Also, moving large volumes of data becomes time-consuming and requires more powerful hardware and software.
Data virtualization simplifies the data integration process. By combining data from different databases, it creates a single integrated platform. This platform then becomes a single point of access for users. It also provides real-time data for reporting and analyses and offers on-demand integration.
Simplifies application data access
Accessing distributed data types and sources is a significant challenge when working with applications. One may need to write hundreds of code lines to simplify sharing data assets among distributed applications. It may also require complex data transformations, which is only possible through virtualization tools or techniques.
For instance, if one has two datasets stored in two databases, virtualization can help fetch the data by automatically executing separate queries for each database. The data is then integrated into a single centralized platform, providing virtual views through a semantic presentation layer.
Data virtualization can significantly impact data analytics, data warehouses, data lakes, and application data access. Organizations can also implement virtualization in big data environments like storage systems, networks, servers, and applications. Any organization looking to stay one step ahead of the game must note the burgeoning trend of data virtualization.