La expresi\u00f3n "big data" es relativamente nueva, aunque el concepto involucrado no lo es. Colecciones de conjuntos grandes y crecientes de datos han existido durante a\u00f1os. Lo nuevo, sin embargo, es cu\u00e1nto "m\u00e1s grande" es el n\u00famero de estos datos, qu\u00e9 tan r\u00e1pido est\u00e1n creciendo, y tambi\u00e9n lo complicado que es acceder a la informaci\u00f3n relevante. Por ello la recolecci\u00f3n y an\u00e1lisis de datos se est\u00e1n convirtiendo en cada vez m\u00e1s valioso para mejorar los procesos y rendimientos. En la referencia, se encuentra una colecci\u00f3n de algunos recursos \u00fatiles para los fines enunciados.<\/p>\n
The search for scientific information<\/strong> usually starts with free internet search engines. Google Scholar, for example, searches through the scientific literature. Wikipedia, probably the most extensive encyclopedia, provides quick information and a good start into a new topic.<\/p>\n However, an ordinary search engine only detects a small part of the contents of the Internet. A complete literature review, therefore, requires additional search methods and skills.<\/p>\n The catchphrase “big data” is fairly new, although the concept is not. Collections of large and growing sets of chemical data have existed for years. What is new, however, is how much “bigger” that data is and how fast it is growing, and also how complicated the information is. Therefore, tools are needed to collect and analyze the data. And collecting and analyzing data is becoming increasingly valuable to improve processes and performance.
\nSo, altogether good reasons to make yourself familiar with databases. Below, you can find a collection of some useful resources.<\/p>\n