- Apache Hadoop
- Data Pipelines
- Data Processing
- Data Warehousing
- Databricks
- PySpark
- SQL
- Data Quality
- MLOps (Machine Learning Operations)
- DevOps
- Apache Spark
- Data Transformation
Spark, Hadoop, and Snowflake for Data Engineering
Completed by Brandon Daryl Wanji Wanmeni
September 30, 2024
29 hours (approximately)
Brandon Daryl Wanji Wanmeni'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
