hashi-ui
dagster
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hashi-ui | dagster | |
---|---|---|
2 | 46 | |
1,235 | 10,173 | |
- | 4.8% | |
0.0 | 10.0 | |
about 1 year ago | 4 days ago | |
JavaScript | Python | |
MIT License | 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.
hashi-ui
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Harbormaster: The anti-Kubernetes for your personal server
Nomad also scales really well. In my experience swarm had a lot of issues with going above 10 machines in a cluster. Stuck containers, containers that are there but swarm can't see them and more. But still i loved using swarm with my 5 node arm cluster, it is a good place to start when you hit the limit of a single node.
> The only serious downsides is having to use the HCL DSL ( https://github.com/hashicorp/hcl ) and their web UI being read only in the last versions that i checked.
1. IIRC you can run jobs directly from UI now, but IMO this is kinda useless. Running a job is simple as 'nomad run jobspec.nomad'. You can also run a great alternative UI ( https://github.com/jippi/hashi-ui ).
2. IMO HCL > YAML for job definitions. I've used both extensively and HCL always felt much more human friendly. The way K8s uses YAML looks to me like stretching it to it's limits and barely readable at times with templates.
One thing that makes nomad a go-to for me is that it is able to run workloads pretty much anywhere. Linux, Windows, FreeBSD, OpenBSD, Illumos and ofc Mac.
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Looking for non-dev friendly batch job operation service
Hashicorp Nomad combined with Hashi-ui (https://github.com/jippi/hashi-ui) comes relatively close, but is disqualified because it provides no support for easy to use provisioning. Azkaban comes relatively close, but seems not to have strong supoort for containers.
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?
hashi-up - bootstrap HashiCorp Consul, Nomad, or Vault over SSH < 1 minute
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
Portainer - Making Docker and Kubernetes management easy.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
watchtower - A process for automating Docker container base image updates.
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
consul - Consul is a distributed, highly available, and data center aware solution to connect and configure applications across dynamic, distributed infrastructure.
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.
dokku-scheduler-nomad - Scheduler plugin for deploying applications to nomad
MLflow - Open source platform for the machine learning lifecycle
swarmpit - Lightweight mobile-friendly Docker Swarm management UI
meltano