Big data existed long before the phrase was invented. For instance, banks and stock exchanges have been processing billions of transactions daily for years and airline companies have worldwide real-time infrastructures for operational management of passenger booking, and so on. So, what is big data really? Doug Laney (2001) suggested that big data is defined by three Vs: volume, velocity, and variety. Therefore, to answer the question of whether your data is big, we can translate this into the following three sub-questions:

• Volume: Can you store your data in memory?
• Velocity: Can you process new incoming data with a single machine?
• Variety: Is your data from a single source?

If you answered all of these questions with yes, then your data is probably not big, and you have just simplified your application architecture.

If your answer to all of these questions was no, then your data is big! However, if you have mixed answers, then it's complicated. Some may argue that one V is important; others may say that the other Vs are more important. From a machine learning point of view, there is a fundamental difference in algorithm implementation in order process the data in memory or from distributed storage. Therefore, a rule of thumb is: if you cannot store your data in memory, then you should look into a big data machine learning library.

The exact answer depends on the problem that you are trying to solve. If you're starting a new project, I suggest that you start off with a single-machine library and prototype your algorithm, possibly with a subset of your data if the entire data does not fit into the memory. Once you've established good initial results, consider moving to something more heavy duty such as Mahout or Spark.