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Use TypeScript instead of Python for ETL pipelines
- This article explains constructing an ETL pipeline in TypeScript for fetching weather data from OpenWeatherMap API and COVID-19 statistics from GitHub, transforming them, and loading into a PostgreSQL database using Prisma.
- TypeScript provides static typing and async/await for clearer API interactions and error handling, minimizing runtime errors from external data inconsistencies compared to dynamically typed languages like Python.
- The project setup involves configuring a Node.js project with TypeScript, installing necessary libraries for HTTP requests, scheduling, CSV parsing, and database interactions using npm.
- The extraction phase fetches data from APIs and CSV, ensuring type-safe results through interfaces and runtime validations, leveraging TypeScript's async/await model for consistent and readable asynchronous code.
- Data transformation ensures a coherent schema before database loading, utilizing TypeScript interfaces and runtime validations to catch API data changes early in the process.
- Loading data into PostgreSQL with Prisma involves defining database schema, generating Prisma client, and executing type-safe database operations for secure data insertion.
- Testing the ETL pipeline involves setting up a PostgreSQL instance, configuring environment variables, running migrations, and orchestrating the ETL process to verify correct data loading and retrieval.
- Automating the ETL pipeline with TypeScript entails using node-cron to schedule task execution periodically, integrating extraction, transformation, and loading steps into a scheduled job for automated data processing.
- TypeScript's benefits over Python for ETL pipelines include static type checking, streamlined data transformation, consistent development environment, and cleaner async/await model, resulting in a more maintainable and reliable pipeline.
- The article serves as a proof of concept for utilizing TypeScript in ETL pipelines, offering a unified stack and reduced debugging overhead compared to Python-based workflows.
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