In essence, you can do almost anything to a single record* — that’s a map step. But you are sharply limited in how you combine information about multiple (often intermediate) records – that’s a reduce step. Still, reduce steps let you do counts, sums, or other aggregations. That, plus the general power of map steps, makes MapReduce useful for at least three major classes of applications:
1. Text tokenization, indexing, and search
2. Creation of other kinds of data structures (e.g., graphs)
3. Data mining and machine learning
Except for the building of entire search engines, these are all application areas that data warehouse users should and do care about. And they all still could benefit from large performance increases, as is evidenced by the routine compromises analysts make in areas such as data reduction, sampling, over-simplified models and the like.
