We chose Gensonix for its being first in the market and also for its ability to handle structured and unstructured data as well as transactions and objects.
Adelphi, MD (PRWEB) May 14, 2013
Scientel’s GENSONIX® NoSQL DBMS, Large Data Warehouse Appliance, and BI/Data Analytics technology for Big Data is now recognized at University/Academic levels. The University of Maryland University College promotes advanced concepts in Information Technology and Database Management Sciences. As part of their Graduate program, they conducted a series of webinars on Big Data and Big Data Technology for Graduate Students and Faculty.
The University of Maryland University College chose GENSONIX® NoSQL DBMS for this project for its being first in the market with a truly-scalable NoSQL DBMS, as well as its ability to handle unstructured/structured data, objects/transactions, as well as indexed/non-indexed tables in an all-in-one DBMS system.
Norman T. Kutemperor, President/CTO of Scientel Information Technology, Inc. presented a webinar on April 16, 2013, on Data Base Management Systems for Big Data, featuring the Scientel GENSONIX® NoSQL DBMS. The webinar was hosted by the University of Maryland for graduate students in the Graduate School of Management & Technology. It was conducted by Elena Gortcheva Ph.D., Program Director, Database System Technology, Information & Technology Systems Department.
Scientel’s webinar was the 3rd in a series. The 1st webinar was presented by Mr. Bill Inmon, known as the “Father of Data Warehousing”. He provides consulting and routinely conducts seminars and presentations throughout the country on Business Intelligence and Big Data. The 2nd webinar was presented by Dr. Kirk Borne, Professor of Astrophysics and Computational Science in the George Mason University School of Physics, Astronomy, and Computational Sciences. He is currently working on the design and development of the proposed Large Synoptic Survey Telescope (LSST), a truly Big Data project for astronomy.
Mr. Kutemperor’s presentation focused on how Scientel’s GENSONIX® NoSQL DBMS can be used to solve Big Data issues in both business and scientific environments.
About Scientel Information Technology, Inc.
Scientel Information Technology, Inc. is a U.S.-based, international, systems technology company, operational since 1977. Scientel also designs/produces highly-optimized high end servers, which can be bundled with its "GENSONIX® ENTERPRISE" DBMS software, as a single-source supplier of complete systems for Big Data environments. Scientel also customizes hardware and software for specific applications resulting in higher performance.
Scientel's specialty is advanced NoSQL DBMS design and applications/systems integration for advanced business processes. This includes applications for BIG DATA, commercial intranets, Supply Chain management , IT consulting, support, etc., along with “beyond mainframe-level” Large Data Warehouse Appliance hardware/systems.
GENSONIX® allows very user-friendly data manipulation capabilities found in standard, SQL-based, database management systems, but it goes beyond. IT IS TRULY AN "ALL-IN-1 SQL"--an “All Data Management System” in the form of an ultra-flexible, NoSQL DBMS of perfectly general capabilities and application potentials. It can also function in concert with mainline SQL systems to efficiently handle both structured and unstructured data as a large data warehouse repository. However, it can handle heavy database loads by itself with the aid of the GENSONIX® NSQL©™ query/procedural language. GENSONIX® supports both telnet as well as http interfaces. GENSONIX® is capable of handling TRILLIONS of rows/transactions for BILLIONS of customers, which is a HUGE advantage in “truly Big Data” structured applications.
Business customers can take advantage of Scientel’s capabilities in advanced Business Intelligence and Visual Data Analytics to grow their business by handling Big Data more cost-effectively and with greater insights to remain competitive. Scientific, government, and similar organizations can use these capabilities to efficiently process Big Data, instead of being swamped by it.