Cloud:
Version Control System:
Programming Language - PYTHON:
BIG DATA TOOL AND SOFTWARES:
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Project Introduction:
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"I worked on an individual data analysis project using Apache Hive. The project involved delving into a dataset related to car insurance, with the goal of uncovering valuable insights and patterns."
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Problem Statement:
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"The main challenge for me was to analyze this dataset and derive meaningful conclusions. I wanted to understand customer behavior, identify trends, and see how various factors, like job categories, age groups, and communication methods, influenced the outcomes."
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Data Loading:
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"To get started, I had to load the dataset into Hive. I created an external table with the provided schema and loaded the data from a text file or an HDFS path. This step allowed me to start working with the data effectively."
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Data Exploration:
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"I began by exploring the dataset:
- I counted the number of records, which was my starting point.
- I found several unique job categories among the customers.
- I grouped customers by age into categories: 18-30, 31-45, 46-60, and 61+.
- I identified and addressed records with missing values to ensure data quality.
- I looked at different 'Outcome' values and their respective frequencies.
- Lastly, I determined how many customers had both a car loan and home insurance."
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Aggregations:
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"I performed several aggregations on the dataset to uncover insights:
- I calculated the average, minimum, and maximum balance for each job category.
- I found the total number of customers with and without car insurance.
- I counted the number of customers for each communication type.
- I summed up the 'Balance' for each 'Communication' type.
- I also looked at the 'PrevAttempts' count for each 'Outcome' type.
- Finally, I compared the average 'NoOfContacts' between customers with and without 'CarInsurance'."
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Partitioning and Bucketing:
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"I then organized the data into partitioned and bucketed tables:
- I created a partitioned table based on 'Education' and 'Marital' status.
- Another table was bucketed into 4 age groups as specified in the project requirements.
- I added an additional partition on 'Job' to the partitioned table and moved data accordingly.
- I increased the number of buckets to 10 in the age bucketed table and redistributed the data."
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Optimized Joins:
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"Optimizing my queries was crucial. I joined the original table with the partitioned and bucketed tables to find valuable insights, such as calculating averages and totals for specific attributes."
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Window Functions:
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"I used window functions for more advanced analysis:
- I calculated cumulative sums, running averages, maximum values, and ranks for different combinations of attributes."
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Advanced Aggregations:
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"For deeper insights, I carried out advanced aggregations:
- I identified job categories with the highest car insurance uptake.
- I pinpointed the month with the highest number of last contacts.
- I calculated the ratio of customers with and without car insurance for each job category."
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Complex Joins and Aggregations:
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"I delved into complex joins and aggregations to understand customer behavior more deeply."
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Advanced Window Functions:
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"I also applied advanced window functions to calculate differences, identify top performers, and compute moving averages."
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Performance Tuning :
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"In the final phase, I experimented with different file formats, compression levels, and Hive optimization techniques to assess their impact on query performance. This was crucial for optimizing my analysis."
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Key Takeaways :
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"In conclusion, this project taught me a lot about data analysis, Hive, and the importance of extracting actionable insights from complex datasets. I learned how to handle real-world data challenges and use advanced techniques to drive meaningful conclusions."