Partition techniques using Pyspark in Databricks
Learning objective
- Understand the concept of maintaining partitions after shuffling the data using partitioning techniques
- Demonstrate the use of partitionBy to organize data based on specific columns.
Overview
In Pyspark a distributed computing framework, you will dive into fundamental concepts and techniques used in PySpark for optimizing data processing and analysis. You'll explore repartitioning, coalesce, broadcast variables, and partition operations to enhance the performance of your Spark jobs.
Story
FoodWagon, a popular food order, and delivery giant, has seen significant growth in the past decade, especially among busy millennials. This surge in popularity has led to a tenfold increase in both the number of orders placed and the number of partnered restaurants.
The Data team, responsible for managing FoodWagon's data, is now faced with the challenge of processing this massive amount of information. The existing system is not suitable for handling the scale of data processing required to provide insights into customer behavior, order patterns, restaurant performance, and more.
Hence, the CDO of Foodwagon has suggested exploring the possibility of leveraging PySpark, a robust data processing engine designed to handle large-scale data. The goal is to implement a PySpark-based solution to enable efficient data processing and real-time analytics for FoodWagon's growing data needs.
The solution must be optimized to process vast volumes of data quickly, providing actionable insights and supporting the decision-making processes within the company. It will be designed to work in harmony with the existing or new storage solutions but will primarily focus on the analytical processing of FoodWagon's burgeoning data.