dagster VS meltano

My recommendation is to continue on with the tutorial, then look at one of the larger example projects especially the ones named “project_”, and you should understand most of it. Of what you don't understand and you're curious about, look into the relevant concept page for the functions in the docs.

I found this example that helped me - https://github.com/dagster-io/dagster/tree/master/examples/project_fully_featured/project_fully_featured

I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home

In Helm terms: there are 2 charts, namely the system: dagster/dagster (values.yaml), and the user code: dagster/dagster-user-deployments (values.yaml). Note that you have to set dagster-user-deployments.enabled: true in the dagster/dagster values-yaml to enable this.

Dagster seemed really cool when I looked into it as an alternative to airflow. I especially like the software defined assets and built-in lineage which I haven't seen in any other tool. However it seems it does not support RBAC which is a pretty big issue if you want a self-service type of architecture, see https://github.com/dagster-io/dagster/issues/2219. It does seem like it's available in their hosted version, but I wanted to run it myself on k8s.

Dagster? https://dagster.io

One that I haven't seen on here yet: dagster

I would use something like meltano or airbyte, but if you really want to use Lambda for extraction I'd say there is no point spinning up a Redshift cluster just for that, Athena would be the way to go and you can use dbt pretty nicely with it and it would keep costs down.

Airbyte and Singer/Meltano if you want to learn more about ingestion pipelines. Airbyte and Meltano teams are very welcoming. SQLfluff a shiny SQL linter. Beautiful project with awesome maintainers.

I know really sure if this is what are you looking for, but take a look at Meltano

I'm not aware of any. I did just open this issue[0] in the Meltano project to open discussion with the team/community. It could be an interesting iteration on the Singer Spec[1] if we find that users are interested in it and it helps solve some bottleneck challenges.

[0] https://gitlab.com/meltano/meltano/-/issues/2616

Good point! As expected, there's an issue about adding it already: https://gitlab.com/meltano/meltano/-/issues/1175

At GitLab, we're not ready to give up on the Singer spec, community, and ecosystem yet, which is why I've been working on Meltano for the past year: https://meltano.com/

We think that the biggest things holding back Singer are the lack of documentation and tooling around taking existing taps and targets to production, and around building, debugging, maintaining, and testing new or existing high-quality taps and targets.

Meltano itself addresses the first problem, and provides a robust and reliable platform for building, running & orchestrating Singer- and dbt-based ELT pipelines.

At the same time, we have been working with some members of the community on a new framework for building taps and targets: https://gitlab.com/meltano/meltano/-/issues/2401, which we have decided to call the Singer SDK: https://gitlab.com/meltano/singer-sdk