getting-started VS astro-sdk

Coincidently, I saw a presentation today on a nice half-way-house solution: using embeddable Python libraries like Sling and dlt - both open-source. See https://www.youtube.com/watch?v=gAqOLgG2iYY There is also singer.io which is more of a protocol than a library, but can also be installed although it looks like it is a true community effort and not so well maintained.

Singer is an open-source framework for data ingestion, which provides a standardized way to move data between various data sources and destinations (such as databases, APIs, and data warehouses). Singer offers a modular approach to data extraction and loading by leveraging two main components: Taps (data extractors) and Targets (data loaders). This design makes it an attractive option for data ingestion for several reasons:

Thanks for chipping in.

I’ve been leaning towards this direction. I think I/O is the biggest part that in the case of plain code steps still needs fixing. Input being data/stream and parameterization/config and output being some sort of typed data/stream.

My “let’s not reinvent the wheel” alarm is going of when I write that though. Examples that come to mind are text based (Unix / https://scale.com/blog/text-universal-interface) but also the Singer tap protocol (https://github.com/singer-io/getting-started/blob/master/doc...). And config obviously having many standard forms like ini, yaml, json, environment key value pairs and more.

At the same time, text feels horribly inefficient as encoding for some of the data objects being passed around in these flows. More specialized and optimized binary formats come to mind (Arrow, HDF5, Protobuf).

Plenty of directions to explore, each with their own advantages and disadvantages. I wonder which direction is favored by users of tools like ours. Will be good to poll (do they even care?).

PS Windmill looks equally impressive! Nice job

Mage uses the Singer Spec (https://github.com/singer-io/getting-started/blob/master/docs/SPEC.md), the data engineer community standard for building data integrations. This was created by Stitch and is widely adopted.

I like the Singer Protocol, and the various tools that use it. These include meltano, airbyte, stitch, pipelinewise, and a few others

Have you looked into using a dedicated data integration tool? Have you heard of Singer and the Singer Spec? https://github.com/singer-io/getting-started/blob/master/docs/SPEC.md

Or you could build your own such system and run it on Airflow, Prefect, Dagster, etc. Check out the Singer project for a suite of Python packages designed for such a task. Quality varies greatly, though.

Our team is building out a data platform on AWS glue, and we pull from a variety of data sources including application databases and third party SaaS APIs. I have been looking into ways to standardize pulling data from different sources. The other day I came across the [Singer Specification](https://github.com/singer-io/getting-started) and was interested learning more about it. If anyone has experience working with Singer specifications, I would love to hear more about:

I just read the readme of the Singer getting started repo and am excited to write my first tap! I’m thinking instead of writing a new Airflow DAG whenever I want to pipe API data into our data warehouse I could write a singer tap and use Stitch instead. Is that a stupid idea?

Have you tried the Astro SDK? https://github.com/astronomer/astro-sdk

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).

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

- Astro SDK*: Made for folks who are doing their ETL in airflow and want to simplify movement between DBs and Pandas

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. 🤞

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!

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.