- Big Data
- Data Transformation
- Databricks
- Data Pipelines
- Data Integration
- SQL
- Apache Spark
- Distributed Computing
- PySpark
- Apache Hadoop
- Data Processing
- MLOps (Machine Learning Operations)
Spark, Hadoop, and Snowflake for Data Engineering
Completed by Vedant Patwardhan
October 27, 2024
29 hours (approximately)
Vedant Patwardhan'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
