This project is a hands-on tutorial on how to use Spark for data processing and analysis, with a focus on Spark SQL, DataFrames, Structured Streaming, and Machine Learning (MLlib). The project includes code examples and explanations for various Spark features and APIs.
- PySpark 3.4.0
- NumPy 1.24.3
- Pandas 2.0.1
Spark SQL is a module in Apache Spark that works with structured data. It allows SQL queries to be seamlessly mixed with Spark programs. PySpark DataFrames allow for efficient reading, writing, transforming, and analyzing of data using both Python and SQL. The same underlying execution engine is used for both Python and SQL, ensuring that the full power of Spark is always utilized.
The project includes examples of creating, manipulating, and transforming RDDs for distributed data processing and analysis.
This section of the project provides explanations and examples of Spark SQL for data processing and analysis, covering common operations such as filtering, grouping, aggregating, and joining.
This section of the project focuses on Spark DataFrames for structured data processing and analysis. It includes examples of creating, manipulating, and querying DataFrames.
The project provides an explanation of Spark Structured Streaming for real-time data processing and analysis. It covers how to create streaming data sources, process data streams, and output the results.
The project includes an explanation of how to use Spark's MLlib library for machine learning tasks such as classification and regression.