Data Engineering using Spark SQL - Basic Transformations - Define Problem Statement

Databases Postgres
1

In this article, we will delve into basic transformations using Spark SQL and walk you through a practical example of calculating daily product revenue. The tutorial is based on a video tutorial available on YouTube which complements the text provided.

[Insert Video Here]

Key Concepts Explanation

Data Preparation

Firstly, we need to prepare the data for our analysis. We will be using the orders and order_items datasets to calculate the daily product revenue. The key fields in the orders dataset include order_id, order_date, order_customer_id, and order_status. On the other hand, the order_items dataset consists of fields such as order_item_id, order_item_order_id, order_item_product_id, order_item_quantity, order_item_subtotal, and order_item_product_price.

We establish a one-to-many relationship between orders and order_items, where orders.order_id serves as the primary key and order_items.order_item_order_id acts as the foreign key to orders.order_id.

Daily Product Revenue Calculation

Our main objective is to calculate the daily product revenue using the orders.order_date, order_items.order_item_product_id, and order_items.order_item_subtotal fields. We will aggregate the subtotal based on the date and product ID to derive the daily revenue.

Hands-On Tasks

To reinforce your understanding, here are some hands-on tasks you can perform:

  1. Load the orders and order_items datasets into Spark SQL.
  2. Filter out only the COMPLETE or CLOSED orders from the orders dataset.
  3. Join the orders and order_items datasets on the respective keys.
  4. Aggregate the subtotal based on the date and product ID to obtain the daily product revenue.

Conclusion

In conclusion, mastering basic transformations using Spark SQL is essential for data processing and analysis. By applying the concepts discussed in this article, you can efficiently calculate metrics such as daily product revenue. I encourage you to practice these techniques and engage with the community for further learning and upskilling.

Define Problem Statement

Let us define the problem statement to gain an overview of basic transformations using Spark SQL. Our goal is to calculate the daily product revenue by leveraging the orders and order_items datasets. We will explore various standard transformations and extract the necessary fields to achieve our objective. Let’s get started!

Watch the video tutorial here