← Big Data & Hadoop learning pathBig Data & Hadoop · Module 4 of 10
MapReduce
Process big data in parallel with map and reduce steps.
📖 Key ideas
- Map step processes input splits into key-value pairs.
- Shuffle and sort groups values by key.
- Reduce step aggregates values per key.
- Classic example: word count.
- Data locality: run computation where data lives.
💡 Real-life example: MapReduce is counting votes: each booth counts locally (map), then totals are combined by candidate (reduce).
MapTransforms input into key-value pairs
ShuffleMoves and groups pairs by key
ReduceAggregates values per key
Data localityCompute near the data