What I thought was a scheduling problem turned out to be a portability problem first The post I Tried to Schedule My ETL Pipeline. Here’s What I Didn’t Expect.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.
Building a production-ready RSS pipeline with Python, Docker, PostgreSQL, and Kestra The post I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer appeared first on Towards Data Science .
A practical data engineering onboarding workflow for environment setup, automated testing, and AI-assisted development. The post Your First Task as a Data Engineer in a New Company?
I tried to make my ETL pipeline production-ready. Three things broke.
A practical walkthrough using text-to-SQL as the example The post Why I Stopped Using One Agent and Built a Multi-Agent Pipeline Instead appeared first on Towards Data Science .
The article describes a real‑world case of scaling an enterprise integration pipeline from 500 to 8,000 events per second. It emphasizes that during this throughput increase, two correctness guarantees were strictly maintained and never compromised. The post illustrates how to achieve high performance while preserving essential data integrity constraints.
The article discusses how the reliability mechanisms added to large language model (LLM) pipelines can lead to confident but incorrect outputs, especially when the correct answer is absent. It examines the behavior of pipelines in such scenarios and highlights the paradox where safeguards intended to improve accuracy may actually reinforce errors. The piece underscores the importance of understanding pipeline responses when faced with missing or ambiguous information.