What is Big Data? Simple question, right? It is not easy to answer any simple question than a complex question. Big Data is explained in Plain English. The big data, is a terminology used to describe sets of data that are becoming so large that they might become difficult to work with conventional database management tools. So, if you are a starter, you must know what is a database. The linked webpage will open in new tab. If you scroll down to that webpage, you will find “Articles Related to Database” like Database and Database System (DBS). If you do not understand the basics, it is not possible to understand what is Big Data.
Obviously, we have lot of articles on various types of database, like, if we go to read this – Indispensable MySQL queries for custom fields in WordPress, we will require to use MySQL Database System. Knowing the fact – MySQL is a database system and there are other types of database system exists, better if you have some idea about MySQL, then the understanding of what is Big Data becomes easy. As we are talking about a scientific thing, there steps to know – we often write this one – its never possible to understand the last chapter of Crammer book without reading the first few chapters. Crammer is also a science and has logics.
Everything on Internet is increasing at quite higher speed. So, in these new orders of magnitude to capture, for storage, retrieval, sharing, analysis, data visualization must be redefined. Prospects processing these huge data are really huge, especially for the analysis of political opinion or industrial trends, genomics, epidemiology and the fight against crime and safety .
The big data phenomenon is considered one of the major IT challenges of the 2010-2020 decade. It generates significant momentum as the administration, by specialists in the field of technology or practice. Big Data offers opportunities to create value far unsuspected. It can help businesses across all sectors, to reduce risk, improve decision making, create a difference through predictive analytics. Big Data and offers a more personalized and contextualized customer experience. If this website has an option to create account for everyone in an unlimited way for the next 5 years – the issue of Big Data will become apparent if we want to calculate, for example, how many males are within 25-34 years age group who clicked the first Advertisement unit.
Dimensions of Big Data
Big Data is accompanied by the development of analytical applications referred to, which process the data to make sense out of it. These analysis are called Big Analytics. They involve complex quantitative analysis of data with the methods of distributed computing.
In 2001, a research report from META Group (now Gartner ) defines the challenges inherent in data growth as three-dimensional: the complex analyzes correspond in fact to the rule known as “3V”, volume, velocity and variety . This model is still widely used today to describe this phenomenon.
Right click and open the image in new window - its quite big.
The global average annual growth rate of technology market and services of Big Data for 2011-2016 is 31.7%. This market is expected to reach $ 23.8 billion in 2016 (according to IDC March 2013).
The amount of stored data is now expanding. According to an IDC study sponsored by EMC Gartner, digital data created in the world would be increased from 1.2 zettabytes per year in 2010 to 1.8 zettabytes in 2011 and 2.8 zettabytes in 2012 and rise to 40 zettabytes in 2020 . For example, Twitter generates currently 7 terabytes of data every day and Facebook 10 terabytes everyday. Yet it is scientific facilities that produce more data.
So, Big Data is really not something that will exactly fall on your head.
Representation of Big Data
The basics of traditional relational data can not manage the data volumes of Big Data. New models of representation can guarantee performance on volumetrics. These technologies, called Business Analytics & Optimization (BAO) to manage massively parallel databases. Architectural patterns like Big Data Architecture framework (BDAF) are offered by market players as MapReduce developed by Google and is used in the framework named Hadoop.
With this system requests are separated and distributed to nodes in parallel and then executed in parallel (map). The results are then collected and Returned (for reduction of size). Teradata, Oracle or EMC (via the acquisition of Greenplum) also can provide such structures, standard-based servers with configurations are optimized. They face competition from vendors such as SAP and more recently Microsoft. Market participants rely on systems with high horizontal scalability and solutions based on the NoSQL (MongoDB, Cassandra) rather than traditional relational data bases.
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