astro-sdk
astronomer-cosmos
astro-sdk | astronomer-cosmos | |
---|---|---|
7 | 8 | |
317 | 449 | |
0.9% | 4.0% | |
8.5 | 9.4 | |
5 days ago | 7 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.
astronomer-cosmos
- Run dbt projects as Apache Airflow DAGs and Task Groups with a few lines of code
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Running dbt core on airflow
You could also try cosmos! https://astronomer.github.io/astronomer-cosmos/
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What's the best way to learn dbt
And rather than a bash operator to trigger your dbt job you can use cosmos to dynamically generate your dbt model as an Airflow job https://astronomer.github.io/astronomer-cosmos/
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PSA: we learned the hard way DBT Cloud support doesn’t work weekends…
Especially now Cosmos is helping to integrate your dbt projects like never before https://github.com/astronomer/astronomer-cosmos
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Best Orchestration Tool to run dbt projects?
Well, I would take a look at Astronomer Cosmos which is a framework to dynamically Airflow DAGs from dbt with a single operator.No need to manage connections, use true BashOperator etc. It’s dead simple and open source https://astronomer.github.io/astronomer-cosmos/ I’m going to make a video about it very soon.
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ELT pipeline using airflow
- Astronomer Cosmos*: Makes converting dbt-core projects into Airflow DAGs drop dead simple
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dbt-cloud along side orchestrator (like AirFlow) in the same company
Check this out: Astronomer Cosmos
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dbt Cloud Alternatives?
With v0.3.0 that we shipped today, we added select/exclude parameters on DbtDag and DbtTaskGroup so that you can filter down to a specific set of models based on dbt tags. In the next couple of weeks, we will add functionality to use select/exclude to filter on Configs, Paths, and Sources. For more info, see the GitHub here (the /examples directory has DAG code that demonstrates DbtDag and DbtTaskGroup
What are some alternatives?
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
airflow-maintenance-dags - A series of DAGs/Workflows to help maintain the operation of Airflow
quadratic - Quadratic | Data Science Spreadsheet with Python & SQL
airflowctl - A CLI tool to streamline getting started with Apache Airflow™ and managing multiple Airflow projects
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]
incubator-airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows [Moved to: https://github.com/apache/airflow]
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."
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
data-aware-orchestration - Data-aware orchestration with dagster, dbt, and airbyte