Data virtualization is a kind of data integration technology that provides access to data in real-time, seamlessly all in one place. In data virtualization, customers can access and manipulate data, regardless of physical location or formatting.
Data virtualization is a kind of data integration technology that provides access to data in real-time, seamlessly all in one place.
Data virtualization tools provide a layer that combines enterprise data from different sources and creates a holistic view of the data. This allows you to apply analytical operations son these views without disturbing the original data.
This article gives comprehensive information on The Top Data Virtualization Tools currently available in the market – their features, applications, use cases, and pricing.
Data Virtualization is an important task that companies that rely on data, need to perform Data Virtualization. The current market offers abundant solutions to do these jobs. Below mentioned are a few favorites that have the maximum potential to solve the tasks at hand.
#1) Data Virtuality Logical Data Warehouse
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Data Virtuality data virtualization tool is designed for organizations that work on relatively large datasets that cannot be processed by other solutions. It also combines data virtualization and ETL together so that it caters to a larger audience.
Features
- It provides real-time data integration along with high-performance query optimization.
- It uses Agile data management, which reduces the access times of data for development.
- It has a large provision for connectors, with more than 200+ connectors readily available.
- It employs data governance for maintaining the quality of data.
Applications and Use cases
Data Virtuality is mostly used in organizations that deal with large datasets. It also provides the historical data and SQL support for query optimization. Data virtuality provides a faster processing environment for datasets that cannot be processed by other tools.
Pricing
The pricing for data virtuality depends on the number of pipelines created. It starts from $249/month for a single pipeline and goes above $599/month as the number of pipelines increases.
1000+ data teams rely on Hevo’s Data Pipeline Platform to integrate data from over 150+ sources in a matter of minutes. Billions of data events from sources as varied as SaaS apps, Databases, File Storage and Streaming sources can be replicated in near real-time with Hevo’s fault-tolerant architecture.
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With Hevo, fuel your analytics by not just loading data into Warehouse but also enriching it with in-built no-code transformations. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Check out what makes Hevo amazing:
- Near Real-Time Replication: Get access to near real-time replication on All Plans. Near Real-time via pipeline prioritization for Database Sources. For SaaS Sources, near real-time replication depend on API call limits.
- In-built Transformations: Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation.
- Monitoring and Observability: Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ETL with Alerts and Activity Logs.
- Reliability at Scale: With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency.
Hevo provides Transparent Pricing to bring complete visibility to your ETL spend.
#2) IBM Cloud Pak for Data
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IBM Cloudpak data virtualization tool is for organizations looking for a converged solution that handles data collection and analysis. The offering was formerly known as IBM Cloud Private for Data and was rebranded in 2018.
Features
- Provides the autoAI feature that helps in deploying ML models efficiently.
- AutoSQL allows automating the data access process, without replicating it.
- AutoPrivacy enables the automatic application of data governance and privacy laws.
- Autocatelog helps in organizing the data for customer relevancy.
Applications and Use Cases
This data virtualization tool is preferred by organizations that require a holistic view of all the operations and data under a single platform. IBM Cloudpak provides a 360 view so that it becomes easier for the users. It also provides an end-to-end AI solution that is efficient in virtualizing data.
Pricing
The IBM Coudpak provides a pay-as-you-go structure. For more information on the pricing, click here.
#3) Atscale Virtual Data Warehouse
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Atscale Data Virtualization tools provide a virtual data warehouse for organizations with prebuilt analytical systems, but just require tools to access the data without actually copying it. This tool is efficient in connecting to business intelligence platforms.
Features
- It is available as both an on-premise system and a cloud solution.
- Atscale’s data abstraction system eradicates the requirement to copy the data.
- It is integrative with all major data visualization and business intelligence tools in a secured format.
- It employs adaptive cache technology, to provide optimized query response.
Applications and Use cases
When the organization has predefined solutions for business intelligence and accepts data warehouse solutions such as BigQuery and Redshift, the Atscale data virtualization tool helps in creating a connection between the sources by making a semantic layer and orchestrating the query performance and transformations.
Pricing
Atscale provides custom pricing for the enterprise’s requirements. To know more about the pricing, click here.
#4) Denodo
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The Denodo data virtualization tool helps in virtualizing data as well as gaining insights from it. The data catalog feature is added to the latest version of Denodo. It is efficient in performing various operations like combining, virtualization of data, identification, and cataloging the data from the source.
Features
- It provides parallel processing for query optimization.
- Operations like connection, governance, and insight generation can be performed without data replication.
- The operations on data can be performed using multiple languages and frameworks like SQL, REST, SOAP, and many more.
Applications and Use cases
The organizations that require proper insight from the enterprise data without replicating and storing it use the Denodo data virtualization tool. This tool provides a semantic search for gaining insights with utmost efficiency. Enterprises also prefer Denodo because it provides data privacy and performs data governance.
Pricing
Denodo provides a free 30days trial of the product post which the pricing is dynamic and depends on the usage. To know more about the pricing, click here.
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Informatica data virtualization tool is for organizations looking for a leading data virtualization tool with integrated data quality tools, PowerCenter is a solid choice. PowerCenter is consistently rated as a top data integration tool by analyst firms for its powerful set of features.
Features
- Uses a no-code platform and provides an interactive GUI to virtualize data from sources.
- Impact analysis provides a predicament of the impacts before even the changes take place.
- The data archiving feature is useful to save storage by archiving unused data.
Applications and Use cases
Informatica PowerCenter data virtualization tool is preferred by enterprises that not only require virtualization of data but also require the quality of data to be good. Informatica has inbuilt data quality tools. Also, the data validation helps in protecting the data damage while movement, saving a lot of valuable time and resources for the organization.
Pricing
PowerCenter provides a dynamic pricing structure. To know more about the pricing, click here.
#6) Oracle Data Service Integrator
Oracle data virtualization tool is for those organizations that are already making use of other Oracle applications for data storage and analytics. Oracle Data Service Integrator is an obvious and easy choice to make for data virtualization.
Features
- This tool has the provision to read and write data from multiple data sources.
- Sophisticated auditing software is provided for securing the data.
- Security is at the forefront, and it is easily connected to other oracle applications
Applications and Use Cases
Oracle Data Integrator is for the organizations that have been using the oracle application suite. This tool is best where the security of data is of utmost importance. Also, it provides real-time access to data and provides a graphic modeling capability for integration.
Pricing
Oracle provides on-demand pricing for the Data Integrator. To know more about pricing, click here.
#7) Red Hat JBoss Data Virtualization
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Red Hat Data Virtualization tool is a good choice for developer-led organizations and those that are using microservices and containers, to build and enable a virtual data layer that abstracts disparate data sources.
Features
- A GUI based on eclipse IDE makes it easier for developers.
- Provides an in-memory database to accelerate the queries.
- Optimized for cloud platforms and is a lightweight platform.
Applications and Use Cases
It is suitable for enterprises with microservices and containers. This tool provides a standardized environment for APIs where it can be connected with sources.
Pricing
Red Hat provides an on-demand pricing solution. To know more about pricing, click here.
#8) TIBCO Data Virtualization
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TIBCO acquired the data virtualization application technology from Cisco in 2017 and has steadily improved it in the years since then. The ability to easily enable data to be used in other applications is a key capability.
Features
- The unstructured data is supported by TIBCO and a built-in transformation engine.
- The data views can be mane from the data directory.
- TIBCO data virtualization tool enables virtualized data to be made available as a data service using auto-generated WSDL
Application and Use cases
This data virtualization tool is best for enterprises that have the requirement for unstructured data and also require an alternative to data cataloging.
Pricing
Tibco offers a 30-day free trial and posts that the basic plan is for $400/month and a premium plan is for $1500/month. It also provides a custom plan. To know more about pricing, click here.
#9) SAP Hana
SAP Hana is an in-memory database. It combines all the platforms of SAP, where it can be deployed both on-premise and cloud platforms. It was released in the year 2010 and written in C++.
Features
- The platform is highly scalable and easily integrable with the cloud.
- Analytic tools combined with AI tools provide a broader vision.
- An in-memory database reduces the total cost of ownership.
Applications and Use Cases
This data virtualization tool is suitable for organizations that use SAP applications and require a solution that seamlessly blends with applications. Also, the operations can be performed without data duplication.
Pricing
SAP Hana provides an on-demand pricing solution. To know more about pricing, click here.
#10) Cdata
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Cdata is a Data virtualization tool that provides a driver to connect to various sources. This tool acts as an on-premise standalone system, but instead of a physical server, the Cdata data virtualization layer helps in orchestrating data.
Features
- Cdata drives can connect with various no-code solutions like Denodo, TICO, and many more.
- It has universal support for ODBC, JDBC, and python.
Applications and Use Cases
This platform is suitable to combine the performance of other applications and act as a virtual Data virtualization layer. It has a large connector base when compared to the other tools.
Pricing
Cdata provides an on-demand pricing solution. To know more about pricing, click here.
Frequently Asked Questions ( FAQs )
What is a Data Virtualization tool?
Data Virtualization Tools help in creating a holistic overview of the data. This view can be used to perform operations on the data without even knowing its original location. Data Virtualization tools help in reducing costs and reducing errors.
How is Data Virtualization done?
Data Virtualization is done through a middle layer, which is a layer that provides virtual access to various data sources and unifies them. This layer provides information on data sources based on demand and even updates in real time.
Why do we need Data Virtualization?
Data Virtualization helps an application process, access, and modify data without requiring technical aspects of data like location information. This helps in protecting the data as well as performing operations on it a lot more efficiently.
What are different Data Virtualization Use Cases?
Data Virtualization is used under various circumstances. A few major use cases include Data Integration, Logical Data Warehouses, Big Data, and Predictive Analytics, Operational Uses, Abstraction, and Decoupling.
Conclusion
The market is moving towards data and many new techniques and technologies are being developed to cater to different needs and provide the utmost efficiency.
Data Virtualization helps in creating views and performing operations on data without actually moving the data.
Also, Data virtualization protects the original data and saves storage by creating thin copies so that operations can be performed on it.
This article provided a comprehensive guide on the Top 10 Data Virtualization Tools in the market.
Since Data Virtualisation is closely related to the ETL process, choosing a proper ETL tool can help in getting maximum insights from the data. This is where Hevo comes into the picture and helps you to seamlessly extract, transform and load data from multiple data sources to data warehouses.
visit our website to explore hevo
Processing data has many techniques. The data pipelines are efficient in mostly all the operations that can be performed on that data.
Data Pipeline requires data and the first step is Data Ingestion, which also employs aspects of Data Virtualization.
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Arsalan is a data science enthusiast with a keen interest towards data analysis and architecture and is interested in writing highly technical content. He has experience writing around 100 articles on various topics related to data industry.