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Based on our record, Apache Airflow seems to be a lot more popular than SimpleCommands. While we know about 75 links to Apache Airflow, we've tracked only 2 mentions of SimpleCommands. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Not quite sure if it’ll work, but it’s worth giving Simple Commands. Source: over 3 years ago
You might want to give Simple Commands a try. This will let you automate the process via web hooks which should give you a link to the default page you desire. You could also use it to create a geo-fence which you could use to arm/disarm your system based on your location. You could even use SC to create shortcuts which can be used via iPhone or Apple Watch. Source: over 3 years ago
Is this really true? Something that can be supported by clear evidence? I’ve seen this trotted out many times, but it seems like there are interesting Apache projects: https://airflow.apache.org/ https://iceberg.apache.org/ https://kafka.apache.org/ https://superset.apache.org/. - Source: Hacker News / 2 months ago
Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python. The tool's greatest advantage is its compatibility with any system or process you are running. This also eliminates manual intervention and increases team productivity, which aligns with the principles of Platform Engineering tools. - Source: dev.to / 3 months ago
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... - Source: dev.to / 4 months ago
Data pipelines: Apache Kafka and Airflow are often used for building data pipelines that can continuously feed data to models in production. - Source: dev.to / 4 months ago
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.``. - Source: dev.to / 5 months ago
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