You and your team were hired to process data using MapReduce. Your company has access to a dataset with commercial transactions between countries during the past 30 years. For each transaction, the dataset contains the following variables:
| ID | Variable (column) | Description |
|---|---|---|
| 00 | Country | Country involved in the commercial transaction |
| 01 | Year | Year in which the transaction took place |
| 02 | Commodity code | Commodity identifier |
| 03 | Commodity | Commodity description |
| 04 | Flow | Flow, e.g. Export or Import |
| 05 | Price | Price, in USD |
| 06 | Weight | Commodity weight |
| 07 | Unit | Unit in which the commodity is measured, e.g. Number of items |
| 08 | Amount | Commodity amount given in the aforementioned unit |
| 09 | Category | Commodity category, e.g. Live animals |
Given the aforementioned context, you are in charge of developing a set of solutions that allow the company to answer the following demands:
- (Easy) The number of transactions involving Brazil✅;
- (Easy) The number of transactions per year✅;
- (Easy) The number of transactions per flow type and year✅;
- (Easy) The average of commodity values per year✅;
- (Easy) The average price of commodities per unit type, year, and category in the export flow in Brazil✅;
- (Medium) The maximum, minimum, and mean transaction price per unit type and year✅;
- (Hard) The most commercialized commodity (summing the quantities) in 2016, per flow type✅.