|over 4 years ago||6 days ago|
|Apache License 2.0||Apache License 2.0|
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We haven't tracked posts mentioning dumbo yet.
Tracking mentions began in Dec 2020.
In the context of Python what is a Bob Job?
2 projects | reddit.com/r/learnpython | 10 Jul 2022
Maybe if your use case is “smallish” and doesn’t require the whole studio suite you could check out apscheduler for doing python “tasks” on a schedule and luigi to build pipelines.
Lessons Learned from Running Apache Airflow at Scale
12 projects | news.ycombinator.com | 23 May 2022
What are you trying to do? Distributed scheduler with a single instance? No database? Are you sure you don't just mean "a scheduler" ala Luigi? https://github.com/spotify/luigi
DevOps Fundamentals for Deep Learning Engineers
6 projects | reddit.com/r/deeplearning | 20 Feb 2022
MLOps is a HUGE area to explore, and not surprisingly, there are many startups showing up in this space. If you want to get it on the latest trends, then I would look at workflow orchestration frameworks such as Metaflow (started off at Netflix, is now spinning off into its own enterprise business, https://metaflow.org/), Kubeflow (used at Google, https://www.kubeflow.org/), Airflow (used at Airbnb, https://airflow.apache.org/), and Luigi (used at Spotify, https://github.com/spotify/luigi). Then you have the model serving itself, so there is Seldon (https://www.seldon.io/), Torchserve (https://pytorch.org/serve/), and TensorFlow Serving (https://www.tensorflow.org/tfx/guide/serving). You also have the actual export and transfer of DL models, and ONNX is the most popular here (https://onnx.ai/). Spark (https://spark.apache.org/) still holds up nicely after all these years, especially if you are doing batch predictions on massive amount of data. There is also the GitFlow way of doing things and Data Version Control (DVC, https://dvc.org/) is taken a pole position there.
Data pipelines with Luigi
4 projects | dev.to | 22 Dec 2021
At Wonderflow we're doing a lot of ML / NLP using Python and recently we are enjoying writing data pipelines using Spotify's Luigi.
Noobie who is trying to use K8s needs confirmation to know if this is the way or he is overestimating Kubernetes.
3 projects | reddit.com/r/kubernetes | 20 Oct 2021
Open Source ETL Project For Startups
3 projects | dev.to | 22 Sep 2021
💡【About Luigi】 https://github.com/spotify/luigi Luigi was built at Spotify since 2012, it's open source and mainly used for getting data insights by showing recommendations, toplists, A/B test analysis, external reports, internal dashboards, etc.
Using Terraform to make my many side-projects 'pick up and play'
3 projects | dev.to | 14 Jun 2021
So to sum that up, I went from having nothing for my side-project set up in AWS to having a Kubernetes cluster with the basic metrics and dashboard, a proper IAM-linked ServiceAccount support for a smooth IAM experience in K8s, and Luigi deployed so that I could then run a Luigi workflow using an ad-hoc run of a CronJob. That's quite remarkable to me. All that took hours to figure out and define when I first did it, over six months ago.
PyPy Project looking for sponsorship to add support for Apple Silicon
5 projects | news.ycombinator.com | 31 Dec 2020
I used Luigi  to automate data processing at a previous job. It's a simple job queue with a UI. You request jobs from it, and then run them for minutes or hours, so it shouldn't normally be a bottleneck and it makes sense to use a language that's quick and easy to write.
It's written in Python and works fine to process thousands of jobs per day. Once you start having tens of thousands of jobs in the queue, it gets slow enough that it can back things up. This compounds the problem, eventually resulting in the whole thing crashing.
By switching the interpreter to PyPy, I was able to keep the data pipeline running at that scale without having to rewrite anything.
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Kedro - A Python framework for creating reproducible, maintainable and modular data science code.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
streamparse - Run Python in Apache Storm topologies. Pythonic API, CLI tooling, and a topology DSL.
Dask - Parallel computing with task scheduling
BPMN_RPA - Robotic Process Automation in Windows and Linux by using Diagrams.net BPMN diagrams.
dpark - Python clone of Spark, a MapReduce alike framework in Python