Big Data analytics requires the right hardware and ideally the right NoSQL type DBMS to handle the loads with high storage efficiency and speed.
Bingham Farms, MI (PRWEB) June 18, 2013
Big Data can in practice be considered: Structured data greater than 20 TB; or Unstructured data greater than 1 TB; or Data acquired from outside sources in large volumes.
Big data is everyone's data today. Big data used to be only for large corporations that needed to handle large amounts of structured commercial data. This is not current reality. Big data is now in practice defined as: Structured data greater than 20 TB; or Unstructured data greater than 1 TB; or a combination of these when speaking strictly in terms of size. Large volumes and high input/processing speed requirements are also characteristic of Big Data.
But speaking in terms of group or class, Big Data is often referred to also as data that is not generated in-house, but is acquired and utilized for purposes such as analytics and business intelligence. Consider the following example: An insurance company releases a customized version of a policy for teenage drivers. An agent previews the sales statistics and realizes he has not met the quota of expected sales. They are definitely in the need to make a decision whether to keep carrying the policy or not. Often times, a totally wrong decision is made by dropping the program based on in-house data statistics only. In this case, Big Data can be brought in to make a better-informed decision and solve the problem faced by the agency. The agency can acquire sales statistics for that type of policy from around the state as well as from around the country. Either of the data sets could provide proper analytics with regard to the sales statistics of the particular policy across the state and across the country and would allow the agency to make the right decision. Assuming the policy sells well elsewhere within the state, the agency can take a certain course of action to adjust their sales methodologies. However in the case of the policy not selling well within the state but doing well around the country, the agency can take a different route to fine tune their marketing strategy as well as modify the policy structure in conjunction with their underwriters. In either case they are able to make the proper decision to keep carrying the program by addressing the deficiencies in both situations. This example illustrates how Big Data Analytics is brought to bear in even relatively small/average business environments. However, Big Data analytics requires the right hardware and ideally the right NoSQL type DBMS to handle the loads with high storage efficiency and speed. GENSONIX® is such a NoSQL DB which provides both efficiencies.
Another way of looking at the Big Data landscape today is that the “old order” primarily utilized SQL DBs with SQL queries. These are admittedly fast enough for reasonable data sizes and simple to use. SQL queries are typically done by management-level or other high-level, trained personnel. The “new order” however utilizes “New DB” technology, powered with the most cost-effective analytics technology today, which is Visual Analytics. As its name implies, Visual Analytics is highly graphical/visual in nature (i.e., “A picture is worth 1000 words”); extremely simple to use; and provides much faster results for large volumes of data (a primary Big Data quality). The fact that virtually anyone can run it is a huge advantage in getting understandable results fast. This allows many more people within an organization to gain more extensive and valuable decision-making results faster. Visual Analytics also provides more insights by unlocking more hidden facts about the data, which requires multiple SQL queries. Much better/faster decisions overall are the result.
New analytics approaches such as Visual Analytics require fresh DB approaches such as NoSQL DBs. These DBs feed data fast to analytics platforms. A “lean, mean” NoSQL DB such as GENSONIX® stores, processes, and delivers data fast, accurately, and reliably to Visual Analytics platforms.
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. GENSONIX® NoSQL DBMS was released in 2003, the 1st NoSQL DB in the market for mainline structured data management. 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 various applications for Big Data in BI and Visual Analytics environments. Scientel also provides 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.