- Data Transformation
- Data Processing
- SQL
- Databricks
- Big Data
- Python Programming
- Data Integration
- Data Warehousing
- Apache Hadoop
- Data Pipelines
- MLOps (Machine Learning Operations)
- Data Quality
Spark, Hadoop, and Snowflake for Data Engineering
Completed by NGAKOUTOU GUELBE SOPHONIE
March 7, 2024
29 hours (approximately)
NGAKOUTOU GUELBE SOPHONIE'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
