- Data Warehousing
- PySpark
- Database Architecture and Administration
- SQL
- Big Data
- Distributed Computing
- Databricks
- Data Integration
- Data Pipelines
- DevOps
- MLOps (Machine Learning Operations)
- Apache Spark
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
