Spark, Hadoop, and Snowflake for Data Engineering
Completed by Mario Jiménez Gutiérrez
November 10, 2024
29 hours (approximately)
Mario Jiménez Gutiérrez'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
- Category: Data Pipelines
- Category: Data Processing
- Category: Snowflake Schema
- Category: DevOps
- Category: PySpark
- Category: Model Training
- Category: Distributed Computing
- Category: Databricks
- Category: MLOps (Machine Learning Operations)
- Category: Data Quality
- Category: Model Deployment
- Category: Big Data
