Course Outline
Introduction
Survey of Data Access Methods (Hive, databases, etc.)
Overview of Spark Features and Architecture
Installation and Configuration of Spark
Grasping Dataframes in Spark
Establishing Tables and Importing Datasets
Querying Data Frames with SQL
Executing Aggregations, JOINs, and Nested Queries
Data Upload and Access
Querying Various Data Types
- JSON, Parquet, etc.
Querying Data Lakes via SQL
Troubleshooting
Summary and Conclusion
Requirements
- Proficiency in SQL queries
- Programming experience in any language
Target Audience
- Data analysts
- Data scientists
- Data engineers
Testimonials (2)
I liked that it was practical. Loved to apply the theoretical knowledge with practical examples.
Aurelia-Adriana - Allianz Services Romania
Course - Python and Spark for Big Data (PySpark)
The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.
