- Python Programming
- Distributed Computing
- Apache Hadoop
- Databricks
- DevOps
- Data Warehousing
- Data Pipelines
- PySpark
- SQL
- Database Architecture and Administration
- MLOps (Machine Learning Operations)
- Data Transformation
Spark, Hadoop, and Snowflake for Data Engineering
Completed by 修瑞 張
May 12, 2024
29 hours (approximately)
修瑞 張's account is verified. Coursera certifies their successful completion of Spark, Hadoop, and Snowflake for Data Engineering
What you will learn
Create scalable data pipelines (Hadoop, Spark, Snowflake, Databricks) for efficient data handling.
Optimize data engineering with clustering and scaling to boost performance and resource use.
Build ML solutions (PySpark, MLFlow) on Databricks for seamless model development and deployment.
Implement DataOps and DevOps practices for continuous integration and deployment (CI/CD) of data-driven applications, including automating processes.
Skills you will gain
