typhoon-orchestrator
Mage
typhoon-orchestrator | Mage | |
---|---|---|
14 | 77 | |
29 | 7,050 | |
- | 3.5% | |
0.0 | 9.9 | |
over 1 year ago | 5 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
typhoon-orchestrator
- After Airflow. Where next for DE?
- New OSS Orchestrator - Where should we go next?
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Airflow's Problem
I have my own opinion on Airflow's pain points and created Typhoon Orchestrator (https://github.com/typhoon-data-org/typhoon-orchestrator) to solve them. It doesn't have many stars yet but I've used it to create some pipelines for medium sized companies in a few days, and they've been running for over a year without issues.
In particular I transpile to Airflow code (can also deploy to Lambda) because I think it's still the most robust and well supported "runtime", I just don't think the developer experience is that good.
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Data Engineering for very small businesses. Any experiences?
Typhoon Orchestrator This is a framework that I designed to help fix some of the pain points of Airflow so that I could build test and deploy pipelines faster. You could skip this step but if you want more info check here.
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CSV data library to database
I am also collaborating on an open source tool called Typhoon Orchestrator (repo). It aims to make composing airflow data pipelines simple and quite quick. Putting pipeline steps together like lego.
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Recommendations for simple ETL (Postgres to Snowflake)
The project (https://github.com/typhoon-data-org/typhoon-orchestrator) doesn't have many stars yet but I have deployed it on a medium sized hotel chain for several data sources with a similar use case to yours and it's been working for over a year with no intervention. If you decide to pursue this option I'd be willing to provide provide some support free of charge (feel free to PM me).
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Impress your friends! Make a serverless bot that sends daily jokes to a Telegram Group
Typhoon Orchestrator is a great way to deploy ETL workflow on AWS Lambda. In this tutorial we intend to show how easy to use and versatile it is by deploying code to Lambda that gets a random joke from https://jokeapi.dev once a day and sends it to your telegram group.
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My Thirty Years of Dodging Repetitive Work with Automation Tools
I think there's space for an open source library that can help with what you described. We originally created https://github.com/typhoon-data-org/typhoon-orchestrator to orchestrate ETL workflows, which would be a superset of the use cases you described. Our next goal is to allow deployment to AWS lambda which can be a good compromise between getting locked in with SAAS and hosting your own infrastructure.
Also check out Zappa's scheduled tasks that have a similar goal and inspired our library.
- Airflow, you complete me! Compose YAML DAGs for Airflow with auto-complete with Typhoon (Open Source).
- Use Airflow? Composable elegant YAML DAGS that transpile to Airflow. Zero risk and no migration.
Mage
- FLaNK AI-April 22, 2024
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A mage on the Hero’s Journey: a fantasy epic on how a startup rose from the ashes
In the coming years, Mage will create a cooperative experience so that developers can build data pipelines with their team and level up together. After that journey, Mage will go on an epic quest to create the 1st open world community experience in the data universe.
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Data sources episode 2: AWS S3 to Postgres Data Sync using Singer
Link to original blog: https://www.mage.ai/blog/data-sources-ep-2-aws-s3-to-postgres-data-sync-using-singer
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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Mage Battlegrounds: Craft insights from real-time customer behavior analysis
You're invited to participate in the very first Mage Battlegrounds: Craft insights from real-time customer behavior analysis, a 24-hour virtual hackathon hosted by Shashank Mishra! This data engineering competition will take place on Saturday, April 15, 2023 beginning at 11am (PST). This will be a global event open to all participants who register.
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Looking for an open-source project
Try this feature: https://github.com/mage-ai/mage-ai/issues/1166
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Daskqueue: Dask-based distributed task queue
Seeing if we can use it in https://github.com/mage-ai/mage-ai
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Data Pipeline on a Shoestring
That being said there’s a solid family of services just breaking ground that make the local pipeline deployment easier (check out https://www.mage.ai, which does have a clear path to cloud deployment of locally developed pipes, it just isn’t well documented yet, and also https://www.neuronsphere.io - which doesn’t have a public solution YET (they’re internally testing an alpha) but they built a cloud deployable solution for their paying customers and working to release one for freemium use)
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Trending ML repos of the week 📈
7️⃣ mage-ai/mage-ai
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Delta without using Spark
Yes, check out how Mage does it: https://github.com/mage-ai/mage-ai/tree/master/mage_integrations/mage_integrations/destinations/delta_lake_s3
What are some alternatives?
JokeAPI - REST API that serves uniformly and well formatted jokes in JSON, XML, YAML or plain text format that also offers a great variety of filtering methods
dagster - An orchestration platform for the development, production, and observation of data assets.
astro - Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow. [Moved to: https://github.com/astronomer/astro-sdk]
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
astro-sdk - Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow.
vscode-dvc - Machine learning experiment tracking and data versioning with DVC extension for VS Code
pachyderm - Data-Centric Pipelines and Data Versioning
sqlmesh - Efficient data transformation and modeling framework that is backwards compatible with dbt.
getting-started - This repository is a getting started guide to Singer.
mito - The mitosheet package, trymito.io, and other public Mito code.
jmespath.py - JMESPath is a query language for JSON.
Data-Science-Roadmap - Data Science Roadmap from A to Z