spark-rapids
dagster
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| spark-rapids | dagster | |
|---|---|---|
| 3 | 46 | |
| 720 | 10,215 | |
| 4.2% | 5.2% | |
| 9.8 | 10.0 | |
| 7 days ago | 4 days ago | |
| Scala | Python | |
| Apache 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.
spark-rapids
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Open source contributions for a Data Engineer?
His newer project, Ballista, was also donated to Apache Arrow. I hope to get the Rust skills to collaborate with him on open source work someday too. He's also doing really cool work on spark-rapids FYI.
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I am reading this article https://www.frontiersin.org/articles/10.3389/fnins.2015.00492/full and thinking how to create an Amazon EMR infrastructure wih PySpark. Why is the GPU server not one of the nodes in the Apache Spark cluster? Or this is just an abstract view and the nodes are also the GPUs?
The spark-rapids project allows one to run multi-GPU ETL workloads on a Spark cluster. https://github.com/NVIDIA/spark-rapids In such a setup, the GPU nodes are part of the Spark cluster. Multi-GPU nodes are viable, although an executor is currently limited to a single GPU.
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Ballista: New approach for 2021
So, in my day job at NVIDIA, I work on the RAPIDS Accelerator for Apache Spark, which is an open-source plugin that provides GPU-acceleration for ETL workloads, leveraging the RAPIDS cuDF GPU DataFrame library.
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?
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.
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
streamlit - Streamlit — A faster way to build and share data apps.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
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
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
meltano - Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.
MLflow - Open source platform for the machine learning lifecycle
meltano