edX
edXSpark, Hadoop, and Snowflake for Data Engineering
123 studentsBEGINNEREnglish
Korshub's take
Data Engineering: Platform Stack & Lifecycle
Modern data systems are built on a distributed stack: storage (Hadoop), processing (Spark), cloud data warehouses (Snowflake), and ML lifecycle management (MLflow). This course teaches you how they fit together and when to use each.
You'll code in PySpark, master the platforms' optimization techniques, and learn to think like a data engineer: building reliable pipelines, applying DevOps discipline to data workflows, and treating the ML lifecycle as end-to-end.
For aspiring data engineers, data scientists expanding into engineering, and software developers transitioning into data roles with hands-on, practical skill-building.
About this course
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.
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