airflow-docker
projects
airflow-docker | projects | |
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2 | 19 | |
223 | 77 | |
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3.0 | 4.7 | |
2 months ago | 3 months ago | |
Python | Jupyter Notebook | |
- | Apache License 2.0 |
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airflow-docker
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ETL with python
You can watch my Apache Airflow for Beginner Tutorial Series playlist on YouTube. If you think it is helpful, consider subscribing to my youtube channel and star my GitHub repository. Comment what topics you want to see or discuss about Airflow in the next episode.
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Apache Airflow for Beginners Tutorial Series
If you are interested, you can watch the whole playlist on YouTube. If you think it is helpful, consider subscribing to my youtube channel and star my GitHub repository.
projects
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Analyze and plot 5.5M records in 20s with BigQuery and Ploomber
You can look at the files in detail here. For this tutorial, I'll quickly mention a few crucial details.
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Three Tools for Executing Jupyter Notebooks
Ploomber is the complete solution for notebook execution. It builds on top of papermill and extends it to allow writing multi-stage workflows where each task is a notebook. Meanwhile, it automatically manages orchestration. Hence you can run notebooks in parallel without having to write extra code.
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OOP in python ETL?
The answer is YES, you can take advantage of OOP best practices to write good ETLs. For instance in this Ploomber sample ETL You can see there's a mix of .sql and .py files, it's within modular components so it's easier to test, deploy and execute. It's way easier than airflow since there's no infra work involved, you only have to setup your pipeline.yaml file. This also allows you to make the code WAY more maintainable and scalable, avoid redundant code and deploy faster :)
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What are some good DS/ML repos where I can learn about structuring a DS/ML project?
We have tons of examples that follow a standard layout, here’s one: https://github.com/ploomber/projects/tree/master/templates/ml-intermediate
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Anyone's org using Airflow as a generalized job orchestator, not just for data engineering/ETL?
I can talk about the open-source I'm working on Ploomber (https://github.com/ploomber/ploomber), it's focusing on seamless integration with Jupyter and IDEs. It allows an easy mechanism to orchestrate work for instance, here's an example SQL ETL and then you can deploy it anywhere, so if you're working with Airflow, it'll deploy it there too but without the complexity. You wouldn't have to maintain docker images etc.
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ETL with python
I recommend using Ploomber which can help you build once and automate a lot of the work, and it works with python natively. It's open source so you can start with one of the examples, like the ML-basic example or the ETL one. It'll allow you to define the pipeline and then easily explain the flow with the DAG plot. Feel free to ask questions, I'm happy to help (I've built 100s of data pipelines over the years).
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What tools do you use for data quality?
I'm not sure what pipeline frameworks support this kind of testing, but after successfully implementing this workflow, I added this feature to Ploomber, the project I'm working on. Here's how a pipeline looks like, and here's a tutorial.
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Data pipeline suggestions
Check out Ploomber, (disclaimer: I'm the author) it has a simple API, and you can export to Airflow, AWS, Kubernetes. Supports all databases that work with Python and you can seamlessly transfer from a SQL step to a Python step. Here's an example.
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ETL Tools
Without more specifics about your use case, it's hard to give more specific advice. But check out Ploomber (disclaimer: I'm the creator) - here's an example ETL pipeline. I've used it in past projects to develop Oracle ETL pipelines. Modularizing the analysis in many parts helps a lot with maintenance.
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Whats something hot rn or whats going to be next thing we should focus on in data engineering?
Yes! (tell your friend). You can write shell scripts so you can execute that 2002 code :) You can test it locally and then run it in AWS Batch/Argo. Here's an example
What are some alternatives?
ansible-docker - Install / Configure Docker and Docker Compose using Ansible.
cookiecutter-data-science - A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
elyra - Elyra extends JupyterLab with an AI centric approach.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
netbox-docker - 🐳 Docker Image of NetBox
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
docker-autocompose - Generate a docker-compose yaml definition from a running container
jitsu - Jitsu is an open-source Segment alternative. Fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days
airflow-docker - This is my Apache Airflow Local development setup on Windows 10 WSL2/Mac using docker-compose. It will also include some sample DAGs and workflows.
Python Packages Project Generator - 🚀 Your next Python package needs a bleeding-edge project structure.