blogpost-airflow-hybrid
Airflow
| blogpost-airflow-hybrid | Airflow | |
|---|---|---|
| 3 | 169 | |
| 9 | 34,570 | |
| - | 1.4% | |
| 0.0 | 10.0 | |
| almost 2 years ago | 5 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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blogpost-airflow-hybrid
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Upgrading my AWS CDK stacks to AWS CDKv2
Once the CDK stacks were validated and clear for deployment, I encountered a "feature" when generating IAM policies and roles. It was whilst fixing a different CDK stack (Orchestrating Hybrid Workflows with Apache Airflow, GitHub repo blogpost-airflow-hybrid that I found that even though the stack had synthesised ok, during the deployment, I got the following error:
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Orchestrating hybrid workflows using Amazon Managed Workflows for Apache Airflow (MWAA)
As always, you can find the code for this walk through in this GitHub repo, blogpost-airflow-hybrid
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Contributing to the Apache Airflow project
My solution was to use Apache Airflow and create a new workflow to orchestrate this. I planned to create an ETL script, and ensure the script can take parameters, to maximise reuse and flexibility.
Airflow
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Building in Public: Leveraging Tublian's AI Copilot for My Open Source Contributions
Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.
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Navigating Week Two: Insights and Experiences from My Tublian Internship Journey
In week Two, I contributed to the Apache Airflow repository.
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Airflow VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Best ETL Tools And Why To Choose
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.
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Simplifying Data Transformation in Redshift: An Approach with DBT and Airflow
Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.
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Share Your favorite python related software!
AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!
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Ask HN: What is the correct way to deal with pipelines?
I agree there are many options in this space. Two others to consider:
- https://airflow.apache.org/
- https://github.com/spotify/luigi
There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…
- "Você veio protestar para ter acesso ao código fonte da urnas. O que é o código fonte?" "Não sei" 🤡
- Cómo construir tu propia data platform. From zero to hero.
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Is it impossible to contribute to open source as a data engineer?
You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte
What are some alternatives?
amazon-ecs-agent - Amazon Elastic Container Service Agent
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
dagster - An orchestration platform for the development, production, and observation of data assets.
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Dask - Parallel computing with task scheduling
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Apache Camel - Apache Camel is an open source integration framework that empowers you to quickly and easily integrate various systems consuming or producing data.
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.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
argo - Workflow Engine for Kubernetes