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Unlock the power of Snowflake and AWS to build robust, scalable data pipelines that integrate seamlessly with your data ecosystem. This course equips you with the tools to design, optimize, and maintain efficient data pipelines, empowering you to master modern data engineering practices. Start by understanding Snowflake's architecture, virtual warehouses, and billing components, and then delve into creating and managing tables, views, and partitions. Explore advanced concepts such as clustering, performance optimization, and query caching while gaining hands-on experience through practical labs. With these foundations, you'll progress to data ingestion, extraction workflows, and continuous data pipelines using Snowflake and AWS S3. Expand your expertise with advanced topics like user-defined functions, external functions, and Snowflake's integration with Python, Spark, and Airflow. Learn to handle real-time data streaming with Kafka and Snowflake, implement governance features like row-level security, and deploy Snowpark for machine learning pipelines. The course culminates in real-world projects that reinforce your knowledge through practice. This course is ideal for data engineers, architects, and cloud professionals seeking to build enterprise-grade pipelines. A foundational understanding of SQL and cloud platforms like AWS is recommended. With its intermediate difficulty, this course bridges the gap between foundational knowledge and advanced data engineering skills.