- Big Data
- Data Warehousing
- Data Quality
- SQL
- Databricks
- PySpark
- Data Processing
- MLOps (Machine Learning Operations)
- Snowflake Schema
- Data Integration
- Apache Spark
- Python Programming
Spark, Hadoop, and Snowflake for Data Engineering
Completed by Xingting Luo
July 31, 2024
29 hours (approximately)
Xingting Luo'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
