Are you tasked with building a data pipeline that integrates weather and public holiday data to analyze how holidays affect weather observation patterns? Or perhaps you need to design a dimensional data warehouse model to track sales performance, inventory levels, and product information for an e-commerce platform? Whatever the challenge, this post will walk you through the key considerations and requirements for building a robust data pipeline and data model.
## API Integration and Data Pipeline
When building a data pipeline, you’ll need to extract historical weather data and public holiday data from two different APIs, transform and merge the data, and model it into a dimensional schema suitable for a data warehouse. Here are the key requirements:
* API Integration: Integrate with Open-Meteo Weather API and Nager.Date Public Holiday API to extract historical daily weather data and public holidays for the corresponding country for the last 5 years.
* Data Transformation: Clean and normalize the data from both sources, combine the two datasets, and flag dates that are public holidays.
* Data Loading: Design a set of tables for a data warehouse to store this data, allowing analysts to easily compare weather metrics on holidays vs. non-holidays.
## E-commerce Data Modeling Challenge
When designing a dimensional data warehouse model for an e-commerce platform, you’ll need to consider the following requirements:
* Data Model Design: Create a star or snowflake schema with fact and dimension tables to store product information, sales transactions, and inventory levels efficiently.
* Schema Definition: Define the tables with appropriate primary keys, foreign keys, data types, and constraints.
* Data Processing Considerations: Explain how your model supports analyzing historical sales with the product prices that were active at the time of sale, and describe how to handle the different granularities of the sales and inventory data.
## Architectural Design Challenge
When designing a data architecture to power a product recommendation engine on an e-commerce website, you’ll need to consider the following requirements:
* Collect Event Data: Track key user interactions such as product_view, add_to_cart, purchase, and product_search.
* Process and Enrich Data: Enrich raw events with user information and product details from other company databases, and transform the event streams into a structured format suitable for analysis and for the recommendation model.
* Make Data Accessible: Provide the real-time processed data to the recommendation engine API, and load the batch-processed data into a data warehouse for analytics.
By following these guidelines and requirements, you’ll be well on your way to building a robust data pipeline and data model that meets the needs of your business.
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*Further reading: Open-Meteo Weather API Documentation and Nager.Date Public Holiday API Documentation