Data Engineering: Kafka, Spark, and Airflow Pipelines

Are you looking to build robust and efficient data pipelines? This course provides a practical approach to data engineering, focusing on the essential tools and techniques used today. You will gain the skills to construct, deploy, and manage contemporary data processing workflows, moving from raw data ingestion to a structured data lakehouse. What you'll learn: - Understand the fundamentals of data engineering and pipeline construction. - Implement streaming and batch data processing patterns with Kafka and Spark. - Manage workflow orchestration and scheduling using Airflow. - Configure and interact with a data lakehouse for scalable data storage. - Apply concepts of schema evolution and data contracts for reliable data flows. - Practice building and deploying pipelines using Docker Compose. This course guides you through setting up a complete data pipeline, from message queuing with Kafka to distributed processing with Spark and data storage in a lakehouse, all orchestrated by Airflow. You will explore essential concepts like windowing, watermarking, and schema management. This course is designed for beginners with a foundational understanding of Python, with no prior data engineering experience required. Start building your modern data pipelines today.