Hey everyone! I'm curious about the cutting-edge tools and technologies you're currently using in your ETL and ELT pipelines. I've recently started using connectorx and DuckDB, and they have really impressed me. Also, I've found that using a logging library in Python has significantly improved how I manage my logs, making it much easier to track my pipelines. What are some other awesome tools or methods you've discovered?
5 Answers
You can't forget about Polars! It's a fantastic library that's really gaining traction for data manipulation. It's fast and efficient for handling large datasets, perfect for ETL workflows.
Ploomber is worth checking out—it's an excellent Python DAG framework that allows you to set up nodes as Python functions with parameters coming from upstream outputs. It supports the IoC pattern and lets you configure tasks with YAML, making it super flexible. You can integrate it with Jupyter, Docker, and Kubernetes too! Plus, it has built-in features for caching, parallel execution, and debugging, which are really handy for managing complex pipelines.
I've been using Prefect along with DuckDB for my ETL stack, and honestly, it's pretty streamlined. If you're working with vector embeddings, consider switching to ONNX runtime models instead of heavier PyTorch ones to keep things efficient.
Which logging library are you finding the most helpful? I'm looking to improve my logging, and I'd love to get some suggestions!
For my data pipeline, I've been using Clickhouse and Apache Airflow. But honestly, I've heard that newer tools like Dagster and Prefect offer a lot more functionality than Airflow. Might have to check those out!
Related Questions
Set Wordpress Featured Image Using Javascript
How To Fix PHP Random Being The Same
Why no WebP Support with Wordpress
Replace Wordpress Cron With Linux Cron
Customize Yoast Canonical URL Programmatically
[Centos] Delete All Files And Folders That Contain a String