Under the definition Big Data, it’s implicit that we are dealing with high volume of data (or maybe not, if we’re dealing with small data). For the organizations, this aspect could become a special challenge if the data is distributed on the structure of the company. But it can be worst, for example, if the company has different data source, some of them with unstructured data.
Here you have five premises of Big Data trying to answer this challange:
- Integrate high data volumes of transactional data and interaction.
- Trust your data.
- Provide auto service to users, analist, developers, data stewards, project owners and usuers of a business.
- Adaptative service.
- Administration of metadata.
It is fundamental for companies that generates high volumes of data to put into practice all necesary actions to govern their Big Data, and that way it can be an input for your business!
But … where is the problem? Of course, having a tool (normally software) that allows you to do all this work, and for this, you need databases, advanced programming languages, statistics, …. A lot of resources!
The software to need to apply Big Data is specific for the objectives you want to achieve. You need a different platform to solve every hipothesis, and sometimes, software infraestructure can be different as well, depending on the case, on the data source, representation, ….
My particular Big Data (not so big)
I think you don’t need high volumnes of data to “practice” Big Data (in my opinion), depending on the problem to solve. I will try to write some post about an app I am developing about soccer and betting, started on 2003 with the only goal og saving data, that has ended being a totally different app, with new and promised goals.
Have a nice day!.
manejando datos, bases de datos, software, programación, visual basic, vba, vb6, mysql, mongodb, bigdata, big data, python, noSQL