cuezel
dagster
cuezel | dagster | |
---|---|---|
1 | 46 | |
12 | 10,274 | |
- | 2.7% | |
0.0 | 10.0 | |
about 3 years ago | 4 days ago | |
Go | Python | |
- | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cuezel
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Dagger: a new way to build CI/CD pipelines
I played with a similar idea a while ago: https://github.com/ecordell/cuezel/ (cuezel as in: "Bazel but with CUE"), but I was never sure that what I was doing was in the spirit of CUE.
CUE pushes nondeterminism into "_tool.cue"[0] files that are allowed to do things like IO and run external processes. Tool files scratch a similar itch to Makefiles, but they lack an integrated plugin system like Bazel (hence why I played with the idea of CUE + Bazel).
With Dagger you seem to be restricted to the set of things that the dagger tool can interpret just with like my Cuezel tool you are limited to what I happened to implement.
In CUE `_tool` files you are also limited to the set of things that the tool builtins provide, but the difference is that you know that the rest of the CUE program is deterministic/pure (everything not in a _tool file).
There's clearly value in tooling that reads CUE definitions, and dagger is the first commercial interest in CUE that I've seen, which is exciting.
But I'm most interested in some CUE-interpreter meta-tool that would allow you to import cue definitions + their interpreters and version them together, but for use in `_tool` files to keep the delineation clear. Maybe this is where dagger is heading? (if so it wasn't clear from the docs)
[0]: https://pkg.go.dev/cuelang.org/[email protected]/pkg/tool
dagster
- Experience with Dagster.io?
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Dagster tutorials
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.
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The Dagster Master Plan
I found this example that helped me - https://github.com/dagster-io/dagster/tree/master/examples/project_fully_featured/project_fully_featured
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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The Why and How of Dagster User Code Deployment Automation
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.
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Best Orchestration Tool to run dbt projects?
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.
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dbt Cloud Alternatives?
Dagster? https://dagster.io
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What's the best thing/library you learned this year ?
One that I haven't seen on here yet: dagster
- Anyone have an example of a project where a handful of the more popular Python tools are used? (E.g. airbyte, airflow, dbt, and pandas)
- Can we take a moment to appreciate how much of dataengineering is open source?
What are some alternatives?
Dagger2 - A fast dependency injector for Android and Java.
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
MLflow - Open source platform for the machine learning lifecycle
meltano
OpenLineage - An Open Standard for lineage metadata collection
streamlit - Streamlit — A faster way to build and share data apps.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
superset - Apache Superset is a Data Visualization and Data Exploration Platform
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production