Load Transform Extract ELT Pipeline for Data Processing

The Load Transform Extract (ELT) pipeline is a crucial step in the data processing workflow, ensuring that data is efficiently loaded into a data warehouse or lake. This article will delve into the intricacies of the ELT pipeline, using Dagster as an orchestration tool to streamline the process.

Data Origin and Cleaning

The data originates from various sources, including MySQL and APIs. The bronze layer serves as the initial storage location for the data, which is then deduplicated, cleaned, and missing values are filled in the silver layer. This ensures that the data is accurate and complete before being transformed into its final form.

Transforming Data with DBT

DBT (Data Build Tool) plays a significant role in transforming the data as needed using PostgreSQL. The ELT pipeline utilizes DBT to create a transform step, which refines the data and prepares it for loading into the gold layer of the data warehouse or lake.

Loading Gold Layer into Data Warehouse

The gold layer is loaded into the data warehouse, specifically PostgreSQL, creating a centralized repository for storing transformed data. This layer serves as the final destination for the data processing pipeline, ensuring that the data is easily accessible and queryable.

Extracting Table-Formatted Data with Polars

Polars, a powerful library for data manipulation, is used to extract table-formatted data from various sources. The extracted data is then loaded into the datalake, MinIO, providing a scalable and flexible storage solution.

Using APIs for Additional Data Collection

In addition to extracting data from APIs, the ELT pipeline also utilizes OpenLibrary API or Google Drive API to collect relevant information such as genre, author, pages number, image, and description. This additional data is crucial in enriching the dataset and providing a more comprehensive understanding of the book recommendations.

Containerization with Docker

To ensure seamless deployment and management, the ELT pipeline uses Docker to containerize the application. Dagster orchestrates the assets defined in its documentation, streamlining the process and minimizing errors.

In conclusion, the Load Transform Extract (ELT) pipeline is a critical component of the data processing workflow, ensuring that data is accurately transformed and loaded into a data warehouse or lake. By leveraging tools such as Dagster, DBT, Polars, and Docker, organizations can efficiently manage their data processing pipelines and make informed decisions based on insights derived from clean and accurate data.

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