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What is Red Hat JBoss Data Virtualization?

Red Hat JBoss Data Virtualization is a powerful tool for virtual data integration, allowing users to unlock and present otherwise inaccessible data in a cohesive and accessible format that can be easily utilized. This solution aggregates data from diverse sources, such as various databases, XML files, and Hadoop systems, presenting them as a unified set of tables within a local database environment. It enables real-time, standards-compliant read and write access to a range of heterogeneous data repositories, enhancing efficiency. By simplifying the process of accessing distributed data, it significantly speeds up both application development and integration efforts. Users are empowered to customize and harmonize data semantics to fit the unique needs of different data consumers. Additionally, the system provides centralized management for access control along with comprehensive auditing capabilities through its robust security framework. Consequently, fragmented data can be swiftly converted into actionable insights, addressing the evolving demands of modern businesses. Furthermore, Red Hat ensures ongoing support and maintenance for its JBoss products throughout designated periods, allowing users to benefit from the latest updates and assistance, thereby enhancing overall operational effectiveness. This commitment to user support reinforces the reliability of the solution in a rapidly changing data landscape.

What is IBM DataStage?

Accelerate the development of AI innovations with the cloud-native data integration solutions provided by IBM Cloud Pak for Data. With AI-enhanced data integration functionalities available from any location, the impact of your AI and analytics initiatives is closely tied to the caliber of the underlying data. Leveraging a contemporary container-based framework, IBM® DataStage® within IBM Cloud Pak® for Data guarantees the provision of high-quality data. This offering combines exceptional data integration with DataOps, governance, and analytics into a cohesive data and AI ecosystem. By streamlining administrative processes, it contributes to a reduction in total cost of ownership (TCO). The platform's AI-driven design accelerators, in conjunction with readily available integrations for DataOps and data science services, significantly expedite the pace of AI development. Moreover, its capabilities for parallel processing and multicloud integration facilitate the delivery of consistent data across extensive hybrid or multicloud environments. Additionally, the IBM Cloud Pak for Data platform allows for the effective management of the complete data and analytics lifecycle, incorporating a range of services such as data science, event messaging, data virtualization, and data warehousing, all supported by a parallel engine and automated load balancing. This all-encompassing strategy equips your organization to remain at the forefront of the swiftly changing data and AI landscape, ensuring that you can adapt and thrive in a competitive market.

Media

Media

Integrations Supported

Azure Marketplace
Hadoop
WebLOAD

Integrations Supported

ActiveBatch Workload Automation
Control-M
FairCom DB
FairCom EDGE
IBM Cloud Pak for Applications
IBM Watson Studio
IRI FieldShield
MettleCI
Origina
Pantomath
Stonebranch

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Red Hat

Date Founded

1993

Company Location

United States

Company Website

access.redhat.com/products/red-hat-jboss-data-virtualization

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/products/infosphere-datastage

Categories and Features

Data Virtualization

Not specified

Categories and Features

Big Data

Not specified

Data Integration

Not specified

Data Lineage

Not specified

Data Mapping

Not specified

Data Virtualization

Not specified

ETL

Not specified

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