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What is Rocket Data Virtualization?
Traditional methods for integrating mainframe data, like ETL, data warehouses, and connector development, are becoming increasingly insufficient in terms of speed, accuracy, and efficiency in the current business environment. With the ever-growing volume of data generated and stored on mainframes, these timeworn approaches are increasingly outpaced. Data virtualization presents itself as a modern solution to this widening gap, streamlining the access of mainframe data for both developers and applications. This innovative method enables organizations to identify and map their data just once, allowing for easy virtualization and reuse across multiple platforms. Consequently, this capability ensures that data remains in alignment with overarching business objectives and goals. By implementing data virtualization on z/OS, organizations can effectively navigate the complexities associated with mainframe resources. Furthermore, data virtualization promotes the integration of information from various disparate sources into a unified logical repository, which significantly enhances the connection between mainframe data and distributed applications. In addition, this technique allows for the enrichment of mainframe data through the incorporation of insights derived from location, social media, and other external datasets, leading to a more holistic understanding of business dynamics. As organizations seek to remain competitive, embracing such advanced methodologies can foster agility and innovation in decision-making processes.
What is DataCurrent?
The real-time observation of rainfall using rain gauges allows operations teams to swiftly recognize possible flooding or sewer overflow incidents. By meticulously monitoring and analyzing precipitation levels across multiple locations, one can estimate rainfall in regions that lack direct measurement, employing the technique known as “Distributed Rainfall Modelling Technique” (DRMT). Moreover, merging rainfall radar data with rain gauge measurements enhances the creation of detailed rainfall coverage maps. Analyzing historical rainfall statistics also aids in developing rainfall intensity-duration curves, which can be matched against the region's design intensity-duration-frequency curves, facilitating the determination of return periods for significant events through forensic examination. Additionally, new intensity-duration-frequency curves can be established, influencing the design of essential drainage infrastructure such as sewers, channels, and storage systems. The ongoing analysis of flow data, in conjunction with rainfall information, leads to the formation of rainfall versus stormwater runoff response curves, which play a crucial role in accurately calibrating drainage models. This thorough methodology guarantees that urban planning and flood management initiatives are not only informed but also adapt effectively to real-time environmental conditions, ultimately enhancing community resilience to extreme weather events.
Integrations Supported
IBM Cloud
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Rocket
Date Founded
1990
Company Location
United States
Company Website
www.rocketsoftware.com/product-categories/data-virtualization
Company Facts
Organization Name
Smart City Water
Company Location
Canada
Company Website
smartcitywater.ca/datacurrent/