buildflow
prefect-deployment-patterns
buildflow | prefect-deployment-patterns | |
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2 | 1 | |
189 | 93 | |
0.0% | - | |
9.4 | 0.0 | |
4 months ago | over 1 year ago | |
Python | 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.
buildflow
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Launch HN: BuildFlow (YC W23) – The FastAPI of data pipelines
One other thing I should mention that's relevent, we do also have a class abstraction instead of a decorator: https://github.com/launchflow/buildflow/blob/main/buildflow/...
This can help with things like setting up RPC clients. But it all boils down to the same runner whether you're using the class or decorator.
prefect-deployment-patterns
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[D] Should I go with Prefect, Argo or Flyte for Model Training and ML workflow orchestration?
Have you used infrastructure blocks in Prefect? You could easily build a block for Sagemaker deploying infrastructure for the flow running with GPUs, then run other flow in a local process, yet another one as Kubernetes job, Docker container, ECS task, AWS batch, etc. Super easy to set up, even from the UI or from CI/CD. There are a bunch of templates and examples here: https://github.com/anna-geller/prefect-deployment-patterns
What are some alternatives?
examples - Organized nonsense.
Taipy - Turns Data and AI algorithms into production-ready web applications in no time.
bytewax - Python Stream Processing
Udacity-Data-Engineering-Projects - Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
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
weather_data_pipeline - This is a PySpark-based data pipeline that fetches weather data for a few cities, performs some basic processing and transformation on the data, and then writes the processed data to a Google Cloud Storage bucket and a BigQuery table.The data is then viewed in a looker dashboard
canarypy - CanaryPy - A light and powerful canary release for Data Pipelines
f1-data-pipeline - F1 Data Pipeline
dataall - A modern data marketplace that makes collaboration among diverse users (like business, analysts and engineers) easier, increasing efficiency and agility in data projects on AWS.
blueprint-examples - This is where you can find officially supported Cloudify blueprints that work with the latest versions of Cloudify. Please make sure to use the blueprints from this repo when you are evaluating Cloudify.
aws-serverless-scheduler - Serverless scheduler webhook, schedule thousands of requests in the future with high precision
DE-ZOOMCAMP-PROJECT