astro-sdk
Mage
astro-sdk | Mage | |
---|---|---|
7 | 77 | |
317 | 7,050 | |
0.9% | 3.5% | |
8.5 | 9.9 | |
5 days ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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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.
astro-sdk
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Orchestration: Thoughts on Dagster, Airflow and Prefect?
Have you tried the Astro SDK? https://github.com/astronomer/astro-sdk
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Airflow as near real time scheduler
One interesting point about putting the data into s3, is that if the data is in an S3 file then OP can use the Astro SDK to pretty easily upload that data into a table or a dataframe (there's even an s3 dynamic task function in the SDK that might fit the use-case well here).
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Most ideal Airflow task structure?
I think you should take a look at the Astro SDK It’s an open source python package that removes the complexity of writing DAGs , particularly in the context of Extract, Load, Transform (ELT) use cases. Look at the doc here, especially aql.transform, aql.run_raw_sql, etc. That will definitely help you
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ELT pipeline using airflow
- Astro SDK*: Made for folks who are doing their ETL in airflow and want to simplify movement between DBs and Pandas
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After Airflow. Where next for DE?
More of a general principle but when you don't have design patterns, you get varying levels of results right? I think what Astro is doing to introduce "strong defaults" through projects like the astro-sdk or the cloud ide are interesting experiments to remove some of the busy work of common dags (load from s3, do something, push to database) will HELP reduce the cognitive load of really common, simple actions and give them a better single pattern to optimize on. I don't think those efforts reduce the optionality of true power users at all who want to custom code their s3 log sink to have some unique implementation while at the same time maybe solving some of the fragmentation to very frequently performed operations. 🤞
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Airflow - Passing large data volumes between tasks
Have you looked into the astro python SDK? My team and I built this out over the last year to do exactly this :). You can you use the `@dataframe` decorator to pull the API data into a dataframe, store it in GCS and the access it in future steps. Lemme know if you have any questions!
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What's the best tool to build pipelines from REST APIs?
I have an example here using COVID data. basically you just write a python function that reads the API and returns a dataframe (or any number of dataframes) and downstream tasks can then read the output as either a dataframe or a SQL table.
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?
quadratic - Quadratic | Data Science Spreadsheet with Python & SQL
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]
vscode-dvc - Machine learning experiment tracking and data versioning with DVC extension for VS Code
starthinker - Reference framework for building data workflows provided by Google. Accelerates authentication, logging, scheduling, and deployment of solutions using GCP. To borrow a tagline.. "The framework for professionals with deadlines."
sqlmesh - Efficient data transformation and modeling framework that is backwards compatible with dbt.
astronomer-cosmos - Run your dbt Core projects as Apache Airflow DAGs and Task Groups with a few lines of code
mito - The mitosheet package, trymito.io, and other public Mito code.
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
airflow-maintenance-dags - A series of DAGs/Workflows to help maintain the operation of Airflow
Data-Science-Roadmap - Data Science Roadmap from A to Z