This repository documents my journey through a PySpark series, aimed at enhancing my understanding of Apache Spark and its practical applications.
The PySpark series consists of multiple projects, with each project focusing on different aspects of PySpark. This repository specifically covers the second project in the series, which delves into fundamental concepts such as data manipulation, querying, and performance optimization within PySpark.
- Comparison between Spark Datasets and DataFrames
- Utilization of Spark SQL for querying structured data
- Recapitulation of Spark SQL and its joins
- In-depth analysis of Spark's performance and optimization techniques
- Understanding query execution plans
- Exploration of Spark User-defined Functions (UDFs)
- Guidance on running Spark jobs locally and in the cloud
- Comparative study of features and enhancements introduced in Spark 3.0 versus Spark 2.0
By engaging with this project, I aim to:
- Strengthen my understanding of PySpark's core components
- Develop proficiency in using RDDs, DataFrames, Spark SQL, and Spark Streaming effectively
- Learn advanced querying capabilities and performance optimization strategies
- Acquire practical skills for real-world data processing and analysis scenarios
- Clone or download this repository.
- Refer to the instructions provided in the project's directories to start exploring PySpark concepts.
- Complete the exercises and experiment with the provided code examples to reinforce learning.
- Extend the project as I progress through the series and gain deeper insights into PySpark.
Special thanks to the course instructor for providing this invaluable learning opportunity.