weather_data_pipeline
prefect-deployment-patterns
weather_data_pipeline | prefect-deployment-patterns | |
---|---|---|
1 | 1 | |
3 | 93 | |
- | - | |
4.2 | 0.0 | |
about 1 year ago | over 1 year ago | |
Python | Python | |
- | Apache License 2.0 |
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weather_data_pipeline
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Building a Weather Data Pipeline with PySpark, Prefect, and Google Cloud
We'll be using PySpark for distributed data processing, Prefect for workflow management, and Google Cloud Storage and BigQuery for data storage and processing.The code is available on github.
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?
magic-the-gathering - A complete pipeline to pull data from Scryfall's "Magic: The Gathering"-API, via Prefect orchestration and dbt transformation.
Taipy - Turns Data and AI algorithms into production-ready web applications in no time.
f1-data-pipeline - F1 Data Pipeline
Udacity-Data-Engineering-Projects - Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.
youtube_data_analysis - Created an optimised pipeline to provide accurate data for analysis, then used snowsight (provided by Snowflake) to create a dashboard.
buildflow - BuildFlow, is an open source framework for building large scale systems using Python. All you need to do is describe where your input is coming from and where your output should be written, and BuildFlow handles the rest. No configuration outside of the code is required.