aws-data-wrangler VS getting-started

AWS Data Wrangler GitHub Repository: https://github.com/awslabs/aws-data-wrangler

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?