In this Real-Time Data Engineering workshop, we walk through the full process of building a real-time ETL pipeline using Apache Airflow:
Data Ingestion: Extracted real-time data from APIs using Airflow Sensors.
Data Transformation: Cleaned and transformed data with Python and SQL directly in Airflow.
Containerization: Used Docker to ensure seamless deployment and reproducibility of the pipeline.
Data Loading: Loaded transformed data into PostgreSQL for storage and analysis.
Orchestration & Scheduling: Managed the entire pipeline with Apache Airflow, ensuring efficient and timely processing.
You can access the code for the Real-Time Ingestion Pipeline featured in our workshop through the following GitHub link:
GitHub Code: https://github.com/AccentFuture-dev/Airflow/blob/Main/Real_Time_Ingestion_Pipeline.py
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