Let's compare different data solutions with the ways of traveling. You will be surprised to find that they have many similarities. When people travel, they either take cars or airplanes depending on the travel distance and cost. For example, when you travel to Vancouver from Toronto, an airplane is always the first choice in terms of the travel time versus cost. When you travel to Niagara Falls from Toronto, a car is always a good choice. When you travel to Montreal from Toronto, some people may prefer taking a car to an airplane. The distance and cost here is like the big data volume and investment. The traditional relational database is like the car in this example. The Hadoop big data tool is like the airplane in this example. When you deal with a small amount of data (short distance), a relational database (like the car) is always the best choice since it is more fast and agile to deal with a small or moderate size of data. When you deal with a big amount of data (long distance), Hadoop (like the airplane) is the best choice since it is more linear, fast, and stable to deal with the big size of data. On the contrary, you can drive from Toronto to Vancouver, but it takes too much time. You can also take an airplane from Toronto to Niagara, but it could take more time and cost way more than if you travel by a car. In addition, you may have a choice to either take a ship or a train. This is like a NoSQL database, which offers characters from both a relational database and Hadoop in terms of good performance and various data format support for big data.