Why Data Engineers LOVE/HATE Airflow (FT.
ฝัง
- เผยแพร่เมื่อ 12 ก.ค. 2022
- Airflow is a favorite tool of many data engineers. But some data engineers dislike it.
It can be tricky to scale and hard to manage if set up incorrectly.
Let's talk about it.
Also, I am an advisor at Mage which is working to make data workflow orchestration easier - www.mage.ai/
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Awesome video, a very balanced perspective without focusing only on strengths or weaknesses of any single tool 👍
Glad you found it helpful! I really was trying to be balanced so I am glad you felt that way.
This was very interesting, glad to hear the different insights. Hope to see more collaborations in the community
That is my goal! I really want to get more perspectives than my own.
Thank you for making this video. I don't want to over promote Airflow because I'm obviously a little bit biased, but I do think a lot of people still know Airflow from version 1.10.X and haven't tried 2.X yet. Many things have been fixed (performances, dag autorhing, UI, etc.). The gap is just huge. Also, I would say the flexibility/freedom that Airflow brings is a double edge sword: You can do a lot, you can configure many things, touch any details to fit perfectly with you needs, but the deeper you go the steeper the learning curve. It's easy to get lost in all features and parameterable things that Airflow brings. However, it's relatively easy IMHO if you just want to run data pipelines and execute a few tasks.❤
Thank you, yeah I think, as you said, most people use Airflow at a very base level. Even if they are using 2.X. Also, I think a while back you may have had a comment on collabing...I feel like I never got back to you on that
What is the best way to learn and practise airflow?
Hi @@SeattleDataGuy . I am seeing lot of people using Airflow to orchestrate ADF in Azure. Is it really worth using it given we already have ADF triggers?
I for one didn't face any issue while working with Xcoms, specially with large dataset using custom backend of Azure Blob storage. And Airflow by design is an orchestrator, so offloading computation is more sensible.
Thanks a lot, much appreciate. I plan to use Apache Kafka on log system. In order to add maintenability to my ETL (transform on Kafka and before ElasticSearch), I wish to add air flow. But Apache Kafka connect look pretty good too. Over this 2 solutions, what will you choose for an ELK + Kafka Pipeline ?
Do a video about Prefect!
Not yet!
X2 😂
Cool journalist approach, glad to have other's opinion included! 👍
Thank you so much for all your perspective on the topic!
Great video! I would love to hear your opinion about Apache Kedro.
It feels like one day it could be great, but I feel like its still early and needs a stronger community before I would adopt it.
Can you review the Meta Database Engineer Professional Certificate on Coursera when it comes out?
We've adopted Argo Workflows, which is a Cloud Native Computing Foundation project built on top of Kubernetes.
Nice! Any pros and cons with that?
What is the different between airflow and astronomer, can you help me sir ?
Thanks for the video.
glad you enjoyed it!
Do a video about flyte
Interesting point of views, thanks for the video. As I see it, technology evolves, but the tech stacks, getting crazy complicated. At the end, mostly it got stuck on the budget, get someone cheap (overpromise data engineer) and you are getting headache, can't move from dev environment and most of the data pipelines are sql at the end. But I could be wrong.
They are getting crazy, keep things as simple as possible for as long as possible
How do you feel about Prefect?
I still haven't got it into production. I believe Madison has a better opinion here. madisonmae.substack.com/p/sorry-i-hate-airflow
Hi
I’m a new subscriber and I just saw your video of “roadmap to become a data engineer” and, I wonder if you could advice me a course to learn python.
You channel is awesome
I don't think there is best ETL pipeline and I would not bother finding the best one. Each company and team operates differently depending on their skillset, Line of Business & priorities. I never had problem while working in SSIS and rarely have problem while working in Data factory either. Yes, each tool have lots of limitations but you will find a way to overcome those limitations.
One thing which I liked about Azure Data Factory is its ease of use with no code and extremely cheap to maintain. Yes, I like to code in Python and work on airflow which gives extreme flexibility which I couldn't have it in ADF but if ADF gives me headache then I will go with this tool anyway. I've onboarded a junior dev who have never worked in any ETL tool in a week. It's that easy.
May time we, data engineers, spend our time tweaking and finding best tools possible in the market but companies hired us to deliver result.
That's fair. Tool wise I think its always up in the air in terms of which is best. I think finding a process that works with the tools you have at hand is probably far more important...because once you switch companies it will be a completely new tool. As you said, results first, fancy tools later.
I’ve also found data factory to work well for orchestrating and the low code keeps it simple. The actual steps being orchestrated are already complex enough.
😅😅
Is Pentaho PDI used for different purposes than Airflow??
I don't think Pentaho is that popular..but i could be wrong. Where do you use it? Have you used it alot?
@@SeattleDataGuy Its used for ETL. But I can mention popular tools.
Im still learnirng and I was wondering if you can ETL,ELT the data with tools like FiveTran/Airbyte/Stitch without using Airflow. Is Airflow used just to automate or you can get the whole ETL process with it?
@@rguez2332 In the past airflow was used for everything. But technically its just an orchestrator. Nowadays people are trying to use other tools like airbyte, dbt + airflow to make pipelines. But thats more for open-source style pipelines. There are so many other tools that people like out there.
@@SeattleDataGuy thx so much!
@@rguez2332 you're welcome!
I'm halfway through this video and I still don't know wtf AIrflow is. I know it has a k8s operator but I have no idea what it is or why I would use it. Maybe this video is for advanced people.
You should summarize pros and cons in the beginning
As a starter in Modern Data Stack , should i learn Prefect or Airflow ? What you recommend
Just pick any one of that, you can always learn the other one later when you need it
Valerian is kind of right. Airflow is far more popular for now. But in tech you're constantly learning. What was important to know this year is old news 3 years from now.
What are peoples thoughts on what data engineer career progression is like because you dont gain a qualification, only work experience???
Do you mean like certificates?
@@SeattleDataGuy yes like how can you keep working your way up without qualifications or in the tech industry do certificates work the same way?
Airflow is amazing.
Why can't you use the well known DevOps tools like Jenkins?
To manage custom data pipelines? I have seen it done. It was pretty hairy though.
Mage AI
woo hoo!
This is a perfect channel. My only criticism is "verbosity". Too many words...
Airflow sucks ....it should be thrown in the trash ......doesn't support muli system, all code based which violates low code-no code rules....impossible to install cluster on standalone mode on premise without involving technologies like docker and kubernetes which increases complexity...etc.