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Based on our record, Meltano should be more popular than Apache Camel. It has been mentiond 26 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.
"correct" is a value judgement that depends on lots of different things. Only you can decide which tool is correct. Here are some ideas: - https://camel.apache.org/ - https://www.windmill.dev/ Your idea about a queue (in redis, or postgres, or sqlite, etc) is also totally valid. These off-the-shelf tools I listed probably wouldn't give you a huge advantage IMO. - Source: Hacker News / 8 months ago
This reminds me more of Apache Camel[0] than other things it's being compared to. > The process initiator puts a message on a queue, and another processor picks that up (probably on a different service, on a different host, and in different code base) - does some processing, and puts its (intermediate) result on another queue This is almost exactly the definition of message routing (ie: Camel). I'm a bit doubtful... - Source: Hacker News / about 1 year ago
Since you're writing a Java app to consume this, I highly recommend Apache Camel to do the consuming of messages for it. You can trivially aim it at file systems, message queues, databases, web services and all manner of other sources to grab your data for you, and you can change your mind about what that source is, without having to rewrite most of your client code. Source: over 1 year ago
For a simple sequential Pipeline, my goto would be Apache Camel. As soon as you want complexity its either Apache Nifi or a micro service architecture. Source: over 1 year ago
🐪 Apache Camel : Camel JBang, A JBang-based Camel app for easily running Camel routes. - Source: dev.to / over 1 year ago
Hey HN, Arch CEO here! Our team has been working at the intersection of data engineering and software engineering for a few years now with Meltano (https://meltano.com), and this year, the rise in Generative AI has made it clear that the bottleneck in unlocking the potential value of data has shifted from data integration on data teams to data engineering on software teams, so we’ve decided to do something about... - Source: Hacker News / 6 months ago
We use Meltano for (EL) and Prefect for scheduling. Is not click-ops, but works very well for us! Behind the scenes Meltano wraps up Singer spec similarly like Airbyte does with its connectors. Before that we tried Airbyte (~5 months ago?) and it was so bad.. We could not choose the columns to replicate and the connectors were unstable i.e. Skipping data, all sort of odd errors and so on.. Source: 12 months ago
Meltano's all-remote team and community of thousands are on a mission to enable everyone to realize the full potential of their data. To this end, we are bringing software engineering best practices to data teams in the form of an open-source DataOps platform that we envision becoming the foundation of every team's ideal data stack. Our public company handbook (https://handbook.meltano.com/) has all the details on... - Source: Hacker News / about 1 year ago
Meltano | Full-Time | Remote | https://meltano.com Meltano's all-remote team and community of thousands are on a mission to enable everyone to realize the full potential of their data. To this end, we are bringing software engineering best practices to data teams in the form of an open-source DataOps platform that we envision becoming the foundation of every team's ideal data stack. Our public company handbook... - Source: Hacker News / about 1 year ago
We switched from AWS Glue to Meltano for the EL part of ELT and it's been a joy to use. We're moving so much faster now. Source: about 1 year ago
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