Learn how to go from writing siloed scripts to building robust, scalable ETL pipelines. This article explores a data engineer's mindset when designing extract, transform, and load processes, integrating tools such as Docker, PostgreSQL, and modern orchestrators. In addition, we look at how process automation, AWS and Azure cloud services, and business intelligence solutions such as Power BI power these architectures. A professional reflection with practical application for companies looking to optimize their data.
My Second ETL Pipeline: Thinking Like a Data Engineer
Learn how to build a production ETL pipeline for RSS with Python, Docker, and PostgreSQL. Learn to think like a data engineer.
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