Welcome to the pyspark_sql_playbook repository! This repository contains a collection of PySpark and SQL-based code examples, playbooks, and solutions for real-world data engineering tasks, including data processing, transformation, aggregations, and performance optimizations.
This repository provides reusable and extendable solutions for big data processing tasks using both PySpark and SQL. It includes playbooks for the following operations:
- Data transformations using PySpark
- SQL-based data querying, manipulation, and optimizations
- Aggregations, filtering, and analysis using both PySpark and SQL
- Performance tuning and optimization techniques in PySpark and SQL
This section contains PySpark-based examples for various data transformations such as:
- Filtering
- Grouping and aggregation
- Joining datasets
- Handling missing data
- UDF (User Defined Functions) usage
Here you'll find SQL-based examples for manipulating and analyzing data using:
- SQL SELECT queries
- JOIN operations in SQL
- Window functions
- Complex aggregations
- Subqueries and common table expressions (CTEs)
This section focuses on performance tuning and optimization strategies in both PySpark and SQL:
- Partitioning and caching techniques
- Query optimization in SparkSQL
- Using broadcast joins effectively
- Reducing data shuffle
- Tuning Spark configurations for better performance