the best videos are usually the most underrated. This video is exactly what I needed. Learning micro servicing for real-time ML applications, and this is so helpful. Thanks a ton for this.
Thank you🙏 for this video. Much needed. Please keeps coming this type of tutorial videos of kafka python with quixstreams. Please do make video on making dataframe with quixstreams (next level or say continuation of this video)
I like how he formatted his code with a single space then a save at around 8:10. Had to fix the syntax error so the formatted could properly parse the Python. Dude is used to coding like this. Epic
It's true, they're so at ease programming in python, it's really enjoyable to watch. Their clear commentary at the same time is like effortless choreography.
Excellent video. Could you please provide some insights on how you configured your vim settings? If possible please make a video about Vim configuration for Python developers.
The big advantage is stream processing. All three libraries support producers and consumers, quixstreams also adds in Kafka Streams-style processing. If you ever need to write the kind of code that reads from one topic and writes to another, that's a huge addition. (There's a new video coming out later today that showcases the stream-processing part of the library. 🥳) For the producer/consumer side, quixstreams is actually using confluent-kafka under the hood, and exposes all the same options, so it's basically a superset of that library. Kafka-Python's big advantage is that it's native Python. Both quixstreams and confluent-kafka wrap the C client, librdkafka. That may be an important factor to you. Though given how good Python wheel support is these days, it's not as big a deal as it used to be. 🤷♂
Hi, nice tutorial! I know it is only an example app, but it’s ok to keep producer open? Would it be better if we open it only just at write time? Thanks. 🙂👍
The big advantage is stream processing. All three libraries support producers and consumers, quixstreams also adds in Kafka Streams-style processing. If you ever need to write the kind of code that reads from one topic and writes to another, that's a huge addition. For the producer/consumer side, quixstreams is actually using confluent-kafka under the hood, and exposes all the same options, so it's basically a superset of that library. Kafka-Python's big advantage is that it's native Python. Both quixstreams and confluent-kafka wrap the C client, librdkafka. That may be an important factor to you. Though given how good Python wheel support is these days, it's not as big a deal as it used to be. 🤷♂
Thanks for the video. I was following the step and got this error: KafkaException: KafkaError{code=_TRANSPORT,val=-195,str="Failed to get metadata: Local: Broker transport failure"}. Can anyone help with this ? Many thanks
There's a problem with the connection to the Kafka broker. This can be due to incorrect broker address, network issues, or broker unavailability. Check out the docs here: quix.io/docs/manage/troubleshooting.html#data-is-not-being-received-into-a-topic Or ask the community for help here join.slack.com/t/stream-processing/shared_invite/zt-2lxwg3a0l-6NIZuvkFVKrm6UTH_97iSA
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the best videos are usually the most underrated. This video is exactly what I needed. Learning micro servicing for real-time ML applications, and this is so helpful. Thanks a ton for this.
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I wish i had a teacher like you!! Things would have been far more better.
how come this video has only 1K likes. gotta appreciate the man's effort
Thanks @Apex543. Please share on socials to help boost the numbers!
Wonderful! I dont do any python nor kafka, but surely its entertaining watching setting up a project from the ground up : )
Thank you! Glad you enjoyed it.
Love your teaching style
Thank you!
I was wondering what Kafka is, I think we could apply this concept at work... A big thank you, you earned a new subscriber
Thank you🙏 for this video. Much needed. Please keeps coming this type of tutorial videos of kafka python with quixstreams. Please do make video on making dataframe with quixstreams (next level or say continuation of this video)
Will do! Stay tuned, there's a video like that planned for this month. :-)
The way you explain is awesome. You are really good at teaching and explaining :)
Thank you! 😃
You just got a new subscriber!!! Simple andl understandable
Awesome, thank you!
So clean and easy explanation. Really like your style.
Thank you 😊
I like how he formatted his code with a single space then a save at around 8:10.
Had to fix the syntax error so the formatted could properly parse the Python. Dude is used to coding like this. Epic
It's true, they're so at ease programming in python, it's really enjoyable to watch. Their clear commentary at the same time is like effortless choreography.
Amazing style of teaching
Thank you for this. It was incredibly easy to follow.
Subscribed, liked and notification turned on. Great video ❤
Legend!
Really good design patterns, thanks for the tutorial
Thanks. Love this
Wow amazing. Thanks a lot!
Excellent video. Could you please provide some insights on how you configured your vim settings? If possible please make a video about Vim configuration for Python developers.
Brilliant! Bravo! 👏👏👏
Thanks 👍
Nice! I didnt know this library. Any advantages/disadvantages over confluent-kafka and kafka-python?
The big advantage is stream processing. All three libraries support producers and consumers, quixstreams also adds in Kafka Streams-style processing. If you ever need to write the kind of code that reads from one topic and writes to another, that's a huge addition.
(There's a new video coming out later today that showcases the stream-processing part of the library. 🥳)
For the producer/consumer side, quixstreams is actually using confluent-kafka under the hood, and exposes all the same options, so it's basically a superset of that library.
Kafka-Python's big advantage is that it's native Python. Both quixstreams and confluent-kafka wrap the C client, librdkafka. That may be an important factor to you. Though given how good Python wheel support is these days, it's not as big a deal as it used to be. 🤷♂
I was looking for streaming data for forecasting model. ❤
Thanks very much for the effort.
Hi, nice tutorial! I know it is only an example app, but it’s ok to keep producer open? Would it be better if we open it only just at write time? Thanks. 🙂👍
Yes, that's fine. It's designed to be a long-running connection. 🙂
❤ learning python
Neat video
why use this over kafka-python or confluent-kafka-python?
The big advantage is stream processing. All three libraries support producers and consumers, quixstreams also adds in Kafka Streams-style processing. If you ever need to write the kind of code that reads from one topic and writes to another, that's a huge addition.
For the producer/consumer side, quixstreams is actually using confluent-kafka under the hood, and exposes all the same options, so it's basically a superset of that library.
Kafka-Python's big advantage is that it's native Python. Both quixstreams and confluent-kafka wrap the C client, librdkafka. That may be an important factor to you. Though given how good Python wheel support is these days, it's not as big a deal as it used to be. 🤷♂
Thanks for the video. I was following the step and got this error: KafkaException: KafkaError{code=_TRANSPORT,val=-195,str="Failed to get metadata: Local: Broker transport failure"}. Can anyone help with this ? Many thanks
There's a problem with the connection to the Kafka broker. This can be due to incorrect broker address, network issues, or broker unavailability.
Check out the docs here: quix.io/docs/manage/troubleshooting.html#data-is-not-being-received-into-a-topic
Or ask the community for help here join.slack.com/t/stream-processing/shared_invite/zt-2lxwg3a0l-6NIZuvkFVKrm6UTH_97iSA
This is neat
Glad you liked it! 🙂
What ide/text_editor are you using?
It's Neovim with some custom configuration. We recommend getting started with the Neovim Kickstart project and tailor to your preferences