- PySpark
- Databricks
- DevOps
- Apache Spark
- Apache Hadoop
- Database Architecture and Administration
- MLOps (Machine Learning Operations)
- Data Processing
- Data Quality
- Data Pipelines
- Big Data
- SQL
Spark, Hadoop, and Snowflake for Data Engineering
Completed by Arnav Dadarya
January 19, 2024
29 hours (approximately)
Arnav Dadarya'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
