Redfin Analytics|python ETL pipeline with airflow|Data Engineering Project|Snowpipe|Snowflake

Project Overview
This project demonstrates a fully automated ETL (Extract, Transform, Load) pipeline developed for Redfin Analytics, utilizing Python, Apache Airflow, AWS, and Snowflake. The pipeline is designed to extract real estate data from Redfin, process and transform it, and then load it into Snowflake for real-time analysis and visualization.

Pipeline Architecture
Data Extraction (Python):

The pipeline initiates with Python scripts extracting real estate data from Redfin’s API. The raw data is stored in Amazon S3 for further processing.
Data Transformation (Python):

Using Python, the raw data undergoes a series of transformation processes, such as cleaning, reshaping, and filtering, to ensure it is optimized for analytics. The transformed data is then stored in a separate S3 bucket.
Orchestration (Apache Airflow):

Apache Airflow, deployed on an AWS EC2 instance, orchestrates the entire workflow. Airflow schedules and triggers each step of the pipeline, ensuring tasks are executed in the correct order and are fault-tolerant.
Data Loading (SnowPipe):

Once transformed, the data is automatically loaded into Snowflake using SnowPipe for real-time ingestion. SnowPipe allows continuous loading of new data into Snowflake for efficient querying.
Visualization:

With the data now available in Snowflake, it can be leveraged for analytics and visualized using various BI tools to uncover real estate trends and insights.