This project is a data engineering pipeline that scrapes data from multiple retailer websites, processes it using Python and Pandas, and saves the transformed data for further analysis and insights.
The Python scraper is responsible for extracting data from various retailer websites. It uses web scraping libraries like BeautifulSoup or Scrapy to fetch the raw data. The scraped data can be saved into different formats, such as files, databases, or sent to Kafka topics for distributed processing.
After the data is collected and stored, Pandas comes into play. It is a powerful Python library for data manipulation and analysis. The raw data is read from the storage or Kafka topics into a Pandas DataFrame. The DataFrame provides a unified tabular structure, making it easier to process and analyze the data.
With the data in a DataFrame, you can perform various data engineering tasks. Data cleaning is done to handle missing values, correct inconsistencies, and remove duplicates. After cleaning, the data can be normalized to a common scale for easy comparison.
Data transformation involves converting the data from one format to another, like converting JSON data to CSV. Additionally, you can aggregate the data by grouping it based on specific attributes, such as time intervals or categories, which helps in creating summaries and generating valuable insights from large datasets.
By leveraging the Python scraper and Pandas, this data engineering pipeline empowers you to collect and process data from multiple retailer websites efficiently. The cleaned and transformed data can be used for further analysis, machine learning, or visualization, giving you valuable insights into the evolving prices and trends in the retail market.