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Based on our record, Apache Airflow should be more popular than Benthos. It has been mentiond 71 times since March 2021. 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.
Streaming and transforming structured documents at scale used to require some awfully complex machinery such as Apache Camel, Kafka Connect, Flink, etc. I was so happy when I bumped into Benthos https://benthos.dev which can be used as a lightweight replacement in most cases. Bonus: It’s written in Golang, so I don’t have to bother with heavy dependencies and slow start times. - Source: Hacker News / 8 months ago
If you're interested in Golang and data streaming, https://benthos.dev is a good project to contribute to. There are quite a few issues open on the GitHub project which anyone can pick up. Writing new connectors and adding tests / docs is always a good place to start. The maintainer is super-friendly and he's always active on the https://benthos.dev/community channels. I'm also there most of the time, since I've... - Source: Hacker News / 10 months ago
I have been working in the stream processing space since 2020 and I used Benthos. Since Benthos is a stateless stream processor, I have other components around it which deal with various types of application state, such as Kafka, NATS, Redis, various flavours of SQL databases, MongoDB etc. Source: over 1 year ago
You might want to add Benthos to your stack. It’s Open Source and it works great for data streaming tasks. You could have your task orchestrator (Airflow, Flyte etc) run it on demand. I demoed it at KnativeCon last year. Source: almost 2 years ago
A few years ago, I found Benthos (the Open Source data streaming processor) and it was really easy to dive into it and add new features. Going through the various 3rd party libraries that it includes is usually straightforward and I'm comfortable enough with the language and various design patterns now to quickly get what's going on. That was rarely the case with C++. Source: almost 2 years 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 / about 1 month ago
There are several tools available that can help manage these workflows. Apache Airflow is a platform designed to programmatically author, schedule, and monitor workflows. - Source: dev.to / 6 months ago
Job scheduling is an important part of data management as it enables regular data updates and cleanups. In a data platform, it is often undertaken by workflow orchestration tools like Apache Airflow and Apache Dolphinscheduler. However, adding another component to the data architecture also means investing extra resources for management and maintenance. That's why Apache Doris 2.1.0 introduces a built-in Job... - Source: dev.to / 6 months ago
Instead of the custom orchestrator I used, a proper orchestration tool should replace it like Apache Airflow, Dagster, ..., etc. - Source: dev.to / 7 months ago
An integral part of an ML project is data acquisition and data transformation into the required format. This involves creating ETL (extract, transform, load) pipelines and running them periodically. Airflow is an open source platform that helps engineers create and manage complex data pipelines. Furthermore, the support for Python programming language makes it easy for ML teams to adopt Airflow. - Source: dev.to / 7 months ago
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