- Data Integration
- Data Transformation
- Data Processing
- Data Pipelines
- Apache Hadoop
- MLOps (Machine Learning Operations)
- Data Quality
- Data Warehousing
- Database Architecture and Administration
- Big Data
- Apache Spark
- Distributed Computing
Spark, Hadoop, and Snowflake for Data Engineering
Completed by Vincent Zhang
November 1, 2024
29 hours (approximately)
Vincent Zhang'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
