Each integration came together quickly: Stripe with just a token, AWS CUR from S3, and GCP billing straight from BigQuery.

This powerful tool stack combined with dlt's connectors, @duckdb.org and @rilldata.com's interactive dashboards, deliver a real-time, consolidated analytics view.

At the core is a single incremental pipeline powered by dlt, loading everything into Parquet & DuckDB for fast analysis.

Handling auth, pagination, and schema changes, the pipeline remains simple end to end, and because it’s fully pluggable, adding Azure or Cloudflare is easy.

This isn't about AI taking jobs. It's about AI exposing what the real job should have been all along.
Building systems of trust. Asking "do I trust this output?" instead of "how do I write this script?"
The intern is here. It's waiting for a manager.

Then the LLMs arrived.
Suddenly, there's an AI intern who can write that heroic query in seconds. An intern who can build a thousand handcrafted pipelines a day.
It's here to automate the firefighter's job into oblivion.

5/5 Read Erfan's full post: https://pipeline2insights.substack.com/p/introduction-to-data-load-tool-dlt

#DataEngineering #Python #dlt #OpenSource #FridayReads

4/5 Whether you're 😤 tired of rebuilding ingestion pipelines, 🔧 looking to integrate Python tools into your data stack or just 💡 curious about modern ELT approaches, this guide has something for you.

3/5 The best part? Anyone who knows Python can build senior-level pipelines.

No new frameworks. No specialized expertise. Just Python doing what it does best.

2/5 What Erfan's guide covers:

⚡ ETL vs ELT & how to avoid data swamps
🐍 Why Python devs need simpler data tools
⚙️ dlt automates schema evolution, incremental loading & data contracts
💻 A practical MySQL-to-DuckDB example you can try today
☁️ Plus real deployment options: Lambda, Airflow, Kubernetes