spark-rapids VS dagster

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.

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.

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.

Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring their evolution, features, and unique strengths, while sharing insights from my hands-on experience with these tools in a weather data pipeline project.

This article demonstrates how to work with near real-time and historical data using the dlt package. Whether you need to scale data access across the enterprise or provide historical data for post-event analysis, you can use the same framework to provide customer data. In a future article, I'll demonstrate how to use dlt with a workflow orchestrator such as Apache Airflow or Dagster.``

4. Dagster

Instead of the custom orchestrator I used, a proper orchestration tool should replace it like Apache Airflow, Dagster, ..., etc.

Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities.

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