. with complex non-SQL Analytics
Big Velocity
. Drink from the fire hose
Big Variety
. Large number of diverse data sources to integrate
- Data volume: terabytes, petabytes, billions of rows per day, hour or minute,
- Data variety: mixing point of sales, call data records, machine generated data, scanned documents, social networking data, smart metering data, structured and unstructured data, which includes data complexity, not only because of data variety but also because data may be really complex to analyze, like video, binary data from M2M communications…
- Need of velocity: big amount of data means data becomes out-of-date very quickly, so it is important to use data as fast as possible.
What is Big Data?
The generally accepted definition of Big Data is data that’s too big to work with – which is to some degree a fallacy on two levels. First, as hardware and software improves, the limit of what’s “too big” is constantly increasing. Second, when people talk about Big Data, it typically isn’t in the context of throwing up their hands in futility, but in finding ways to use existing hardware and software technology to manipulate their data.
This is why Big Data is often just the other side of the coin from data analytics, because analytics uses different ways of slicing, dicing, and otherwise picking through vast amounts of data to find the bits that are interesting and relevant to a particular task.
Not that long ago, organizations that wanted to analyze their data were limited to the size of a floppy disk. But not only is the potential size of a database getting bigger – particularly with new technologies such as the cloud – the industry is figuring out new ways to link together multiple databases into what appears as a single whole. “Logical data warehouses bringing together information from multiple sources as needed will replace the single data warehouse model,” predicted Gartner recently when it named Big Data as one of its Top 10 Strategic Technologies for 2012.
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