Hadoop MapReduce
Large datasets can be processed in parallel and distributed using the MapReduce processing engine and programming style. It is divided into two primary phases: the Reduce phase is where the results from the Map phase are integrated and aggregated to create the final output, and the Map phase is where input is processed and filtered in parallel across several nodes.
Important Steps of MapReduce
Large dataset processing is facilitated by the MapReduce technique, which divides complex jobs into smaller, parallelizable steps.
- Map: During the Map phase, data is divided into smaller splits, with many nodes handling each split separately and concurrently. This stage involves splitting each input split into intermediate key-value pairs using a user-defined map function.
- Shuffle and Sort: Following the Map step, the intermediate key-value pairs are shuffled and sorted according to their keys. This step gathers the values mapped to the same key to get the data ready for the Reduce phase.
- Reduce: In the Reduce phase, intermediate key-value pairs that share the same key are combined. The grouped pairs are then run through a user-defined Reduce function to produce the desired result.
Hadoop : Components, Functionality, and Challenges in Big Data
The technical explosion of data from digital media has led to the proliferation of modern Big Data technologies worldwide in the system. An open-source framework called Hadoop has emerged as a leading real-world solution for the distributed storage and processing of big data. Nevertheless, Apache Hadoop was the first to demonstrate this wave of innovation. In the era of big data processing, businesses across various industries need to manage and analyze internal large volumes of data efficiently and strategically.
In this article, we’ll explore the significance and overview of Hadoop and its components step-by-step.
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