Big Data and Small Data

 In the early 2000s, analyst Doug Laney defined Big Data with three characteristics called “the three Vs”: Volume, Speed, and Variety of Data. However, it has been shown that Big Data is not only science and technology, but that it responds to a strategic vision of the business . In short, in Big Data the volume of data is not as important as the knowledge it provides us and allows us to make better decisions and make better strategic movements.

Until the emergence of Big Data (Massive Data in Spanish), Business Intelligence worked with what we now call Small Data . Today we are in a position to differentiate them:

•            Small Data works with smaller volumes of data , while Big Data works since 2012 with petabytes instead of Terabytes , since data is collected from sources as varied as commercial transactions, Social Media and sensors in machines. There is talk of Big Data from 4 or 5 terabytes, but as we have said in recent years we are already talking about pentabytes.

•            Small Data works with processed and structured data and the management and analysis is made from it , while Big Data manages and analyzes changing data practically in real time.

•            Small data works with data from different sources, but always structured , while Big Data works with varieties of multistructured data, not just numerical structured data; but also unstructured from social networks, e-mail, videos, audios or commercial transactions.

Small Data works with OLTP (Online Data Processing) and EDW (Enterprise Data Warehouse) software for data management and analysis on DBMS (Database Management Systems). The most used database management systems are MySQL, Microsoft Access, SQL Server, FileMaker, Oracle, RDBMS, dBASE, Clipper and FoxPro.

Big Data uses Data Warehouse that manages structured data such as financial records, customer and sales data and combines it with Big Data Systems that store unstructured data. In addition, it incorporates emerging systems such as Hadoop , a free software framework prepared to work with NoSQL Database Management systems (unstructured data) and incorporates Stream Computing to integrate data in motion from different sources, guaranteeing a response in milliseconds.

In short, if up to now our database systems were fed by large volumes of structured data, the complexity that has meant that the data comes from different platforms, added to the seasonality of the same and the data entry peaks; It has required software that allows the management area of the company to manage all that information in order to make better decisions and adopt a correct strategy in an ever-changing business environment to which it is necessary to react quickly.

 

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