The article recounts the author's experience of moving a Dockerized data pipeline from a local laptop to AWS, highlighting the challenges that arose when the environment changed. It explores lessons learned about container behavior, networking intricacies, and hidden assumptions that were previously taken for granted in a local setup. The post serves as a practical guide for developers facing similar transitions to cloud infrastructure.
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 .
Starting with a local Parquet file, then joining it to data stored in the cloud The post Building a Data Lakehouse with DuckDB and DuckLake appeared first on Towards Data Science.
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 .
I tried to make my ETL pipeline production-ready. Three things broke.
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.
This is the opening piece of a four-part deep dive series, on building a high-frequency streaming pipeline against a live public API. The data source is openSenseMap, a citizen-science IoT network used for climate research, mostly in Germany.
A hands-on walkthrough of a hybrid local-cloud workflow using Gemma 4 and GPT-5. 4, with reasoning and structured outputs The post Stop Choosing Between Local and Cloud LLMs: A Field Guide to Hybrid Patterns appeared first on Towards Data Science .
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.
arXiv:2606. 30963v1 Announce Type: cross Abstract: Repository-grounded automated repair is often reported as a single end-to-end capability, which hides distinct failure modes such as poor file targeting, incorrect patch synthesis, and failed iterative debugging.
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.
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?
LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that classifies failures, adapts payloads across model tiers, preserves execution state, and maintains schema integrity during provider swaps.