docker-airflow VS ploomber

I am facing an error while deploying Airflow on Kubernetes (precisely this version of Airflow https://github.com/puckel/docker-airflow/blob/1.8.1/Dockerfile) regarding writing permissions onto the filesystem.

I used https://github.com/puckel/docker-airflow to setup the airflow and I moved my python scripts inside the dags directory but now they won't execute because I can't access the installed libraries in the virtual environment. How can i find a workaround for this?

Used a custom version of Puckel Airflow Docker image (Spent a lot of time customising to our needs, but default Airflow container should still work)

I understand it is subjective. But I use a forked version of https://github.com/puckel/docker-airflow on our managed K8s cluster and it points to a cloud managed Postgres. It has worked pretty well for over 3 years with no-one actually managing it from an infra POV. YMMV. This is driving a product whose ARR is well in the 100s of Millions.

If you have simple needs that are more or less set, I agree Airflow is overkill and a simple Jenkins instance is all you need.

So were running Airflow v1 (based on this docker compose) with a sequential executor running on an on prem OpenShift v3 setup. We have a new / free resource coming and have planned to use them to reinitiate a complete new version utilizing OpenShift v4 (also on prem but not managed by us) and upgrade in parallel to Airflow v2. The question is if anyone has any strong recommendations on a good docker compose file they would look at and any views on celery / kubernets workers. We're not a huge team but have a bit of experience up our sleeves now so was more after some guidance or thoughts if others have gone down similar paths. Thanks!

version: '3' services: postgres: image: postgres:9.6 environment: - POSTGRES_USER=airflow - POSTGRES_PASSWORD=airflow - POSTGRES_DB=airflow ports: - "5432:5432" webserver: image: puckel/docker-airflow:1.10.1 build: context: https://github.com/puckel/docker-airflow.git#1.10.1 dockerfile: Dockerfile args: AIRFLOW_DEPS: gcp_api,s3 PYTHON_DEPS: sqlalchemy==1.2.0 restart: always depends_on: - postgres environment: - LOAD_EX=n - EXECUTOR=Local - FERNET_KEY=jsDPRErfv8Z_eVTnGfF8ywd19j4pyqE3NpdUBA_oRTo= volumes: - ./examples/intro-example/dags:/usr/local/airflow/dags # Uncomment to include custom plugins # - ./plugins:/usr/local/airflow/plugins ports: - "8080:8080" command: webserver healthcheck: test: ["CMD-SHELL", "[ -f /usr/local/airflow/airflow-webserver.pid ]"] interval: 30s timeout: 30s retries: 3

As for learning, learn how to build and use docker containers. For airflow, take a look a https://github.com/puckel/docker-airflow and see how to add you pipelines to that container. Then learn how to do CI/CD for docker containers (tons of tutorials). Then learn to deploy containers, you can use aws ecs.

Usually googling the software you want + docker will get you what you need. For that particular project, I used https://github.com/puckel/docker-airflow to help set up a local airflow instance.

A imagem do docker utilizada foi a puckel/docker-airflow onde acrescentei o BeautifulSoup como dependência para criação da imagem em minha máquina.

We used to schedule and monitor workflows tool airflow as our ELT processor and have to extract data from SQL and No-SQL databases to load them into the warehouse. Our airflow deployment was done through docker, for more details checkout puckel/airflow. Currently, we are adopting our image to the official docker images.

- One-click sharing powered by Ploomber Cloud: https://ploomber.io

Documentation: https://jupysql.ploomber.io

Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README).

We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!

For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel

And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber

Develop notebook-based pipelines

Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.

We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.

I recommend taking a look at the ploomber open source. It helps you structure your code and parameterize it in a way that's easier to maintain and test. Our blog has lots of resources about it from testing your code to building a data science platform on AWS.

Developing end-to-end data science infrastructure can get complex. For example, many of us might have struggled to try to integrate AWS services and deal with configuration, permissions, etc. At Ploomber, we’ve worked with many companies in a wide range of industries, such as energy, entertainment, computational chemistry, and genomics, so we are constantly looking for simple solutions to get them started with Data Science in the cloud.

If you like working locally with notebooks, you can run via the free tier of ploomber, that'll allow you to get the Ram/Compute you need for the bigger models as part of the free tier. Also, it has the historical executions so you don't need to remember what you executed an hour later!

It really depends on your use cases, I've seen a lot of those tools that lock you into a certain syntax, framework or weird language (for instance Groovy). If you'd like to use core python or Jupyter notebooks I'd recommend Ploomber, the community support is really strong, there's an emphasis on observability and you can deploy it on any executor like Slurm, AWS Batch or Airflow. In addition, there's a free managed compute (cloud edition) where you can run certain bioinformatics flows like Alphafold or Cripresso2

That's what we do for lineage with https://ploomber.io/