After the lecture I gave last June to open the conference held in San Sebastián on Big Data applications for businesses, the editors of the journal "DYNA New Technologies" contacted me to ask for a collaboration paper, where I could gather the key ideas presented during my lecture. After organizing my notes using a temporal guiding thread, and after the usual reviewing milestones, my paper "Understanding Big Data: antecedents, origin and later development" was published.
The paper begins with a revision of the antecedents of data analytics applied to businesses, reviewing the main concepts and terms that "share an ecosystem" with Big Data (Business Intelligence, Data Mining, Data Science, ...) and going back to their origins, presenting their definitions as a key to understand Big Data in the right context. On the basis of such a context, I detail what we can consider the origin of Big Data technologies. This key milestone is linked to the innovations launched in the first instance by Google, mainly their MapReduce model and the distributed file system it is implemented on, tools whose dissemination enabled the later development of Apache Hadoop.
From then, I cover a series of milestones and relevant technologies (tools to enhance the analytics capabilities on Hadoop, new NoSQL databases, improvements on Hadoop's original implementation, booming alternatives such as Apache Spark, generic approaches such as Lambda Architecture, etc.) that have been developing around Big Data to this day. In the conclusions, as well as reinforcing the idea that not every Data Mining application is Big Data (despite the usual confusion between those terms), I point at different development and application fields for Big Data as a technology providing value in diverse business contexts.
Here is the open access link to my paper "Understanding Big Data: antecedents, origin and later development".
From then, I cover a series of milestones and relevant technologies (tools to enhance the analytics capabilities on Hadoop, new NoSQL databases, improvements on Hadoop's original implementation, booming alternatives such as Apache Spark, generic approaches such as Lambda Architecture, etc.) that have been developing around Big Data to this day. In the conclusions, as well as reinforcing the idea that not every Data Mining application is Big Data (despite the usual confusion between those terms), I point at different development and application fields for Big Data as a technology providing value in diverse business contexts.
Here is the open access link to my paper "Understanding Big Data: antecedents, origin and later development".
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