- Data Warehousing
- Big Data
- Apache Hadoop
- Data Integration
- Data Transformation
- SQL
- PySpark
- Distributed Computing
- Data Processing
- Data Quality
- Data Pipelines
- Database Architecture and Administration
Spark, Hadoop, and Snowflake for Data Engineering
Completed by Aleksandar Bukvic
August 11, 2024
29 hours (approximately)
Aleksandar Bukvic'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
