earthly
dagster
Our great sponsors
earthly | dagster | |
---|---|---|
17 | 46 | |
10,704 | 9,939 | |
4.7% | 4.7% | |
9.8 | 10.0 | |
6 days ago | 5 days ago | |
Go | Python | |
Mozilla Public License 2.0 | 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.
earthly
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Is your makefile supposed to be a justfile?
earthly
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Containerize CI pipelines with Earthly
# cat Makefile BIN_PATH = $(shell pwd)/bin $(shell mkdir $(BIN_PATH) &>/dev/null) EARTHLY = $(BIN_PATH)/earthly earthly: ifeq (,$(wildcard $(EARTHLY))) curl -L https://github.com/earthly/earthly/releases/download/v0.6.23/earthly-linux-amd64 -o $(EARTHLY) chmod +x $(EARTHLY) endif
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GitHub Actions Is Down
I started to bring awareness to the Earthfiles goofy license, but it seems they've switched to MPL! https://github.com/earthly/earthly/releases/tag/v0.6.15
The (unfortunately named) Dagger is also an entry into that space: https://github.com/dagger/dagger#readme (Apache 2)
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Please name some open source projects which are collecting small user analytics metrics and how
- https://github.com/earthly/earthly/tree/main/analytics
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Dagger: a new way to build CI/CD pipelines
Another *monster* difference is that Dagger is (at least currently) Apache 2: https://github.com/dagger/dagger/blob/v0.2.4/LICENSE but Earthly went with BSL: https://github.com/earthly/earthly/blob/v0.6.12/LICENSE
That means I'm more likely to submit bugs and patches to Dagger, and I won't touch Earthly
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Migrating Your Open Source Builds Off Of Travis CI
Example build steps for a go application
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Earthly: Beyond Docker
sudo /bin/sh -c 'wget https://github.com/earthly/earthly/releases/latest/download/earthly-linux-amd64 -O /usr/local/bin/earthly && chmod +x /usr/local/bin/earthly && /usr/local/bin/earthly bootstrap --with-autocomplete'
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Show HN: Earthly v0.6
Great suggestion! We have a walk-thru in the docs and some examples in GitHub.
https://docs.earthly.dev/basics
https://github.com/earthly/earthly/tree/main/examples
Do those help?
The `earthly ls` idea is great. We do have shell autocompletions but that is not quite the same. I will add a ticket for that.
Cache misses can be a bit inscrutable. It could be the buildkit GC is running, because disk space is getting scarce, or that some arg or file change caused the cache to be considered invalid.
Caching is an area we will continue to improve. We have a proposal for extended cache mounts here[1].
Thanks for using earthly!
We have a PR for the ls feature up:
dagster
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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
In the meantime, we're collecting solutions and use cases in our GitHub Discussions, and you're welcome to ask any specific questions in there!
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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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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
- Can we take a moment to appreciate how much of dataengineering is open source?
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Dagger Python SDK: Develop Your CI/CD Pipelines as Code
I wondered how it related to https://dagster.io/
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Data Engineer Github Profile?
You can find all current, closed, and resolved issues on the “Issues” section and explore them using filters: eg issues for dagster. Look into some of the issues and feel free to ask a question or post your idea: it’s much less toxic here (compared to SO, for example).
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[D] Should I go with Prefect, Argo or Flyte for Model Training and ML workflow orchestration?
You could also consider Dagster, which aims to improve Apache Airflow's shortcomings. Also, take a look at MyMLOps, where you can get a quick overview of open-source orchestration tools.
What are some alternatives?
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
dagger - Application Delivery as Code that Runs Anywhere
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