Database System Concepts
Database System Concepts
7th Edition
ISBN: 9780078022159
Author: Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher: McGraw-Hill Education
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Fundamentals of Datawarehouse
A data warehouse is a data repository which is used to store large quantities of historical data that are mostly used for creating reports that help businesses to identify their weaknesses and strengths. For example, demographics data about people of a r…
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**Problem 3 and 4 request you to resolve various problems using Hadoop.** To get full credit, please explicitly show all steps of converting data from 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. ### Diagram Explanation The diagram illustrates the process of data transformation using Hadoop, which involves several stages: 1. **Splitting:** - The raw data is divided into three parts. These sections represent different segments of the data to be processed. 2. **Mapping:** - Each segment from the splitting step is processed individually. - The data is mapped into key-value pairs, where each key is associated with a corresponding value. 3. **Shuffling:** - The key-value pairs are reorganized based on the key. This step groups all values associated with similar keys together to ensure efficient data processing. 4. **Reducing:** - The shuffled data undergoes reduction, where operations are performed on the values to produce a condensed output. - Again, data is maintained in key-value pairs format. 5. **Final Results:** - The reduced data is compiled into a final result set, representing the processed output. ### Example Suppose we have the document **BigData.txt** below: ``` W M U M U A W M W C A ``` **Expected Result:** - W, 3 - M, 3 - U, 2 - A, 2 - C, 1 This output implies that the letter 'W' appears 3 times, 'M' appears 3 times, 'U' appears 2 times, 'A' appears 2 times, and 'C' appears 1 time after processing through Hadoop.
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Transcribed Image Text:**Problem 3 and 4 request you to resolve various problems using Hadoop.** To get full credit, please explicitly show all steps of converting data from 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. ### Diagram Explanation The diagram illustrates the process of data transformation using Hadoop, which involves several stages: 1. **Splitting:** - The raw data is divided into three parts. These sections represent different segments of the data to be processed. 2. **Mapping:** - Each segment from the splitting step is processed individually. - The data is mapped into key-value pairs, where each key is associated with a corresponding value. 3. **Shuffling:** - The key-value pairs are reorganized based on the key. This step groups all values associated with similar keys together to ensure efficient data processing. 4. **Reducing:** - The shuffled data undergoes reduction, where operations are performed on the values to produce a condensed output. - Again, data is maintained in key-value pairs format. 5. **Final Results:** - The reduced data is compiled into a final result set, representing the processed output. ### Example Suppose we have the document **BigData.txt** below: ``` W M U M U A W M W C A ``` **Expected Result:** - W, 3 - M, 3 - U, 2 - A, 2 - C, 1 This output implies that the letter 'W' appears 3 times, 'M' appears 3 times, 'U' appears 2 times, 'A' appears 2 times, and 'C' appears 1 time after processing through Hadoop.
**Problem 4: Indicating the <Key, Value> pairs in each phase of data processing in Hadoop** Please write each step in bullet points or by drawing diagrams to get the top 2 most frequent keywords in BigData.txt using Hadoop.
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Transcribed Image Text:**Problem 4: Indicating the <Key, Value> pairs in each phase of data processing in Hadoop** Please write each step in bullet points or by drawing diagrams to get the top 2 most frequent keywords in BigData.txt using Hadoop.
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