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Real Estate Data Scraping and Matching with Apache Airflow Automation

Developed a system to scrape real estate data from multiple sources, match relevant data points, and automate the entire workflow using Apache Airflow.

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In the highly dynamic real estate market, access to accurate and up-to-date information is crucial. This project automates the process of scraping real estate data from multiple online platforms, including listings, property features, pricing trends, and geographic information. The system is designed to collect data on a regular basis, ensuring that the information remains relevant and timely.

Key components of the project include:

Data Scraping:
Web scraping scripts were developed to extract property details, pricing, location data, and other key features from various real estate websites. These scripts handle diverse data formats and ensure that data is captured accurately across different platforms.

Data Matching:
The collected data is processed and matched based on specific attributes such as location (city, neighborhood), price ranges, and property types. This matching process helps to organize the data into relevant groupings and provide more insightful comparisons for potential buyers or analysts.

Data Pipeline Automation:
Apache Airflow was employed to automate and schedule the entire data pipeline, from scraping to data processing. Airflow’s DAGs (Directed Acyclic Graphs) were used to define the workflow and dependencies, ensuring data is collected, processed, and stored on time. The pipeline is designed to handle retries, error logging, and notifications, providing robust monitoring and fault tolerance for the process.

Scheduled Data Collection and Updates:
The system is configured to scrape data at predefined intervals, ensuring that the real estate information is always up-to-date. Airflow’s scheduling mechanism allows for efficient handling of periodic tasks and ensures that data collection does not interfere with system performance.

Data Integration:
After matching the data, the results are stored in a central database or data warehouse for further analysis and reporting. This integration allows for easy querying and visualizations, which can be used by analysts, buyers, or real estate professionals to identify trends, compare properties, and make informed decisions.

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