Spark, Hadoop, and Snowflake for Data Engineering

In this course, you will: Explore essential data engineering platforms (Hadoop, Spark, and Snowflake) and learn how to optimize and manage them Delve into Databricks, a powerful platform for executing data analytics and machine learning tasks Hone your Python data science skills with PySpark Discover the key concepts of MLflow, an open-source platform for managing the end-to-end machine learning lifecycle, and learn how to integrate it with Databricks Gain methodologies to help you improve your project management and workflow skills for data engineering, including applying Kaizen, DevOps, and Data Ops best practices This course is designed for learners who want to pursue or advance their career in data science or data engineering, or for software developers or engineers who want to grow their data management skill set. With quizzes to test your knowledge throughout, this comprehensive course will help guide your learning journey to become a proficient data engineer, ready to tackle the challenges of today's data-driven world.

• Optimize and manage Hadoop, Spark, and Snowflake platforms
• Execute data analytics and machine learning tasks using Databricks
• Enhance Python data science skills with PySpark
• Manage end-to-end machine learning lifecycle with MLflow
• Apply Kaizen, DevOps, and DataOps methodologies for data engineering