Redwing Technologies






New database Methodology

Redwing Technologies introduces you to a new database methodology that was recently invented by Datura, LLC (USA). Like with any new technology the Legume Database Container needs fundamental understanding before assessment can be made of its applicability and capability. 


·          Merge or integrate some or all your databases


·          Store variable or object data in a relational database


·          Uniform data containers for easier data distribution


·          Store legacy data in an accessible manner


·          Lower costs of development and maintenance


·          High flexibility of data and development.



Equal Format Databases

Legume is a database container system that can hold all of your data in a single relational database design – the equal format database. Its unique table structure is scalable, reusable and can be built on any database server from mainframe to portable devices. Its tables can be added into existing databases to give them added capabilities. You can build or enhance applications more easily with reusable, reliable components. The uniform structure of Equal Format Databases makes it easier to store, move and manipulate complex data. This new database technology is patent-pending and, with its patents being described by a World Trade Organisation Group as “Novel and Inventive”, is fully operational now.


Time And Cost Saving

Major cost benefits are achieved by using equal format database containers:


·          Cross-platform system design allows use of  almost any database system vendor


·          Standard relational design can be used in current systems thus  preserving past training and tools


·          Repeat  use of a common structure allows building of  reusable, reliable and secure software


·          Ready-to-go common design allows faster application development with less gridlock and easier data distribution


·          Merges databases and updates legacy data.

Unique Benefits


·          Store complex, variable data efficiently in a powerful relational database and in a straightforward manner


·          More flexibility than XML and RDF and just as portable


·          Incremental, non-destructive storage provides regulatory data tracking and efficient data snapshots from any point-in-time


·          Search across columns as easy as rows


·          Store multiple values and types in a field.


Datura, LLC

Datura was founded in 2005 as a software laboratory to develop advanced database technology based on years of experience in large-scale commercial databases, technical databases and off-the-shelf application development. Datura invented the concept of Equal Format Databases and has filed a patent on a robust, commercial version of the technology. Refer to for more information.


The Future of Databases

You are invited to try a single-user version of the technology by downloading BeanCounter, a desktop application built with the Legume database container system. To try the software or read more details, go to the special website at Any objective feedback will be most welcome and if necessary responded to.


Legume is a relational database container built on standard server systems, but differs in that it is already designed, normalized, and ready to use for now and for future changes. It is robust enough to store any kind or mixture of logical data structure - relational, hierarchical, object, or semantic. Because it’s a relational structure it can be integrated into existing relational systems and because it is uniform data management becomes standardized, optimized, and reusable, demonstrated by our standard client program, BeanCounter, which can be used as a platform for other client applications.

Datura’s differentiation is that Legume is an “Equal Format Database”, which is a relational database design where the table structure is independent of the data content and logical structure, making it super-normalized in traditional terms. Datura’s implementation of Equal Format Databases uses a structure and methodology that allows for enterprise-wide and global data management with high-volume transaction and distribution capabilities.


The Legume Database Container Overview

·          Because of its reusability, projects take less time and cost less

·          Because it can hold any kind of data, it can handle future needs

·          Because of its uniform design, data is more easily shared

·          Its design allows easy data merging despite differences among data

·          Its modular system is scalable – from the smallest project to supercomputing

·          Can give the point-in-time data snapshots that engineers and researchers need

·          Excellent for regulation compliance and security data tracking

·          Can revive legacy databases’ usefulness.


Examples - Legume Technology Markets


·          Corporate Database Systems and Enterprise Architecture: Small companies have dozens of databases and large companies have thousands of them. Each database will average a hundred or more tables, each averaging a dozen columns. Any business changes, legal changes, mergers, system enhancements or departmental collaborations lead to database changes. These changes bring the additional burden of adapting the old (legacy) data and software to the new system. Legume System allows system changes easily with its flexible design, saving time and costs.


·          Databases in Engineering and Design: Many of the same problems in corporate systems are shared in the engineering and design world.  It requires data in different formats and analytical models to design and build or operate something. Legume systems use an Equal Format to allow these formats and models to work together.  Gives engineers point in time data retrieval on equipment, power systems, tracking systems etc.


·          Medical / Retail Records Databases:  As medical / retail records are computerized to save costs, different formats and models will be merging and working together.  Compiling, storing and accessing complex data will require flexibility and reliability. Legume technology can handle it, with its unique data container design. All types of data can be easily integrated and equally accessed. 


Examples - Market Situations


·          Legacy Databases: We lower the cost of importing old databases into a new relational database system, and integrating it with the new system, regardless of the original data structure. This is a big cost savings in a project.

·          Mergers: Because you do not have to design a new composite database, we lower the cost of merging databases, especially from different organizational structures. Some mergers can be extremely difficult to do otherwise, especially if one of them is not relational already.

·          Data Tracking Repository with Point-in-Time Retrieval: The atomic nature of the data storage and its time-tracking organization provide an incremental mode of storage that tracks all changes (by user and time) in a way that you can retrieve a frozen snapshot of the database from any point in time of its history as easily as you can retrieve the current state.

·          Regulatory Repository: The flexible nature of the structure easily allows storing any record from any database, and views it with our client viewer, BeanCounter. Accounting and management can peer into the process at all stages.

·          Transactional Data Distribution and Messaging: The flexible, atomic format of our databases allows general data distribution systems to be created that can adapt to changes without breaking the system. This allows publishers to add new data fields without having to synchronize with the receivers of the data, avoiding the gridlock caused by traditional designs.

·          Integration System: The same flexible storage capability needed for mergers is also needed for merging department data. The standard viewer, BeanCounter, makes it easy to view the data used by others without extra programming. Delinking data from structural design relieves the synchronization that causes gridlock.

·          Application Integration Platform: The same flexible storage capability needed for mergers and data integration allows suites of applications to be built that work together more easily without the gridlock caused by integration of traditional designs.

·          Analysis Data and Unusual Logical Structures: Data comes in different structures – mathematical, hierarchical, relational, physical, object, semantic, and mixtures. We avoid much of the difficulty of designing new structures for new each batch of data collected in research and operations.

·          Data Warehousing, Data Marts, and Data Mining: The atomic, relational nature of our design makes easier connections between data from all sources and transformations. New tables do not have to be designed for each question, and business intelligence can become smarter with standardized storage structures.

·          Master Data and Catalog Data: People, places, and things come with great variance in data and interrelationships, and are constantly changing. Searching and manipulating the data is restricted by the work needed to deal with the constant changes added over time. The flexible structure removes much of this burden and allows a path to growth and variance over time.

·          Global Data Management: Our system design offers the unique capabilities of taking our flexible containers, key system, and methodologies to give you the control necessary to provide global data distribution and management.


Examples – Target Market


·          Enterprise Architects

·          Data Architects

·          Chief Information Officers

·          Architects and Engineers

·          Researchers

·          Software Companies.




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