docker-airflow VS orchest

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.

You can check out our OSS https://github.com/orchest/orchest

The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/

First want to say congrats to the Patterns team for creating a gorgeous looking tool. Very minimal and approachable. Massive kudos!

Disclaimer: we're building something very similar and I'm curious about a couple of things.

One of the questions our users have asked us often is how to minimize the dependence on "product specific" components/nodes/steps. For example, if you write CI for GitHub Actions you may use a bunch of GitHub Action references.

Looking at the `graph.yml` in some of the examples you shared you use a similar approach (e.g. patterns/openai-completion@v4). That means that whenever you depend on such components your automation/data pipeline becomes more tied to the specific tool (GitHub Actions/Patterns), effectively locking in users.

How are you helping users feel comfortable with that problem (I don't want to invest in something that's not portable)? It's something we've struggled with ourselves as we're expanding the "out of the box" capabilities you get.

Furthermore, would have loved to see this as an open source project. But I guess the second best thing to open source is some open source contributions and `dcp` and `common-model` look quite interesting!

For those who are curious, I'm one of the authors of https://github.com/orchest/orchest

Haven't tried it. In its favor, Argo is vendor neutral and is really easy to set up in a local k8s environment like docker for desktop or minikube. If you already use k8s for configuration, service discovery, secret management, etc, it's dead simple to set up and use (avoiding configuration having to learn a whole new workflow configuration language in addition to k8s). The big downside is that it doesn't have a visual DAG editor (although that might be a positive for engineers having to fix workflows written by non-programmers), but the relatively bare-metal nature of Argo means that it's fairly easy to use it as an underlying engine for a more opinionated or lower-code framework (orchest is a notable one out now).

If you’d like to try something for you data workflows that’s vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest

You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation

We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest

For people in this thread interested in what this tool is an alternative to: Airflow, Luigi, Kubeflow, Kedro, Flyte, Metaflow, Sagemaker Pipelines, GCP Vertex Workbench, Azure Data Factory, Azure ML, Dagster, DVC, ClearML, Prefect, Pachyderm, and Orchest.

Disclaimer: author of Orchest https://github.com/orchest/orchest