please explicitly show all steps of converting data from a raw data to a final output following the template: For SPLITTING: please split to 3 parts (Hint: Text file has three lines). For MAPPING and REDUCING: please explicitly show which data is key, which data is value. RAW DATA SPLITTING MAPPING key value key value key value Suppose we have the document BigData.txt below WMU MUAW MWCA SHUFFLING REDUCING key value key value key value FINAL RESULTS

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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### MapReduce Data Processing Steps

This tutorial explains the steps to convert raw data into a final output using the MapReduce framework. We will follow these steps:

#### Splitting
- The raw data input is split into parts. For example, if we have a text file with three lines, it should be split into three segments.

#### Mapping
- During this phase, each split of raw data is transformed into key-value pairs. This helps in preparing the data for sorting and shuffling.

#### Shuffling
- Key-value pairs are organized so that all values for a specific key are brought together. This prepares the data for the reducing phase.

#### Reducing
- In this step, the data is processed to produce the final results. The reduce function aggregates values for each key, transforming the shuffled data into a smaller set of results.

#### Diagram Explanation

The diagram illustrates the flow of data through the stages:
1. **Splitting**: Raw data is divided into chunks.
2. **Mapping**: Each chunk is converted into key-value pairs.
3. **Shuffling**: Data is rearranged so that all values sharing a common key are grouped together.
4. **Reducing**: Key-value groups are processed to generate the final results.

#### Example Document: BigData.txt
The content of the example document is as follows:

```
W M U
M U A W
M W C A
```

This document will undergo splitting, mapping, shuffling, and reducing to illustrate the MapReduce process in action.
Transcribed Image Text:### MapReduce Data Processing Steps This tutorial explains the steps to convert raw data into a final output using the MapReduce framework. We will follow these steps: #### Splitting - The raw data input is split into parts. For example, if we have a text file with three lines, it should be split into three segments. #### Mapping - During this phase, each split of raw data is transformed into key-value pairs. This helps in preparing the data for sorting and shuffling. #### Shuffling - Key-value pairs are organized so that all values for a specific key are brought together. This prepares the data for the reducing phase. #### Reducing - In this step, the data is processed to produce the final results. The reduce function aggregates values for each key, transforming the shuffled data into a smaller set of results. #### Diagram Explanation The diagram illustrates the flow of data through the stages: 1. **Splitting**: Raw data is divided into chunks. 2. **Mapping**: Each chunk is converted into key-value pairs. 3. **Shuffling**: Data is rearranged so that all values sharing a common key are grouped together. 4. **Reducing**: Key-value groups are processed to generate the final results. #### Example Document: BigData.txt The content of the example document is as follows: ``` W M U M U A W M W C A ``` This document will undergo splitting, mapping, shuffling, and reducing to illustrate the MapReduce process in action.
Indicating the `<Key, Value>` pairs **in each phase** of data processing in Hadoop, please write **each step** in bullet points or by drawing diagrams on how to count the frequency of each letter in BigData.txt using Hadoop.
Transcribed Image Text:Indicating the `<Key, Value>` pairs **in each phase** of data processing in Hadoop, please write **each step** in bullet points or by drawing diagrams on how to count the frequency of each letter in BigData.txt using Hadoop.
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