rumble VS zingg

As part of my data consulting, I struggled with identity resolution and started working on scalable no code identity resolution - https://github.com/zinggAI/zingg/ . It has pushed my limits as a software engineer and product builder, and I had to do a lot of learning to build it. Its cool to see people use Zingg in their workflows and save months of working on custom solutions. Big highlight has been North Carolina Open Campaign Data https://crossroads-cx.medium.com/building-open-access-to-nc-...

Check https://github.com/zinggAI/zingg/. We recently added Python to our stack and are looking for help with building dbt-zingg python models, databricks-zingg python notebooks, python api, building a python based front end etc.

If you are open to using a framework for this, check Zingg at https://github.com/zinggAI/zingg. It connects to Redshift, snowflake and other warehouses and can handle multiple columns

Thanks for your support. Yes we do ship with some examples and their models which can be run out of the box. We have 3 customer demographic datasets and an ecommerce items matching across Google and Amazon. You can check them here https://github.com/zinggAI/zingg/tree/main/examples

I have an open source project hosted at https://github.com/zinggAI/zingg/.

Check Zingg out at https://github.com/zinggAI/zingg and let me know if you would like to contribute

This is interesting, would love to get your feedback on Zingg(https://github.com/zinggAI/zingg) if you are upto it. Thanks!

I have released an open source entity resolution tool Zingg(https://github.com/zinggAI/zingg). Zingg uses Spark and ML to build single source of truth directly in the warehouse or the datalake. Would love to hear from the Reddit folks here what they think about it - do you find it useful? what can I do to make it better? any advice on the problem or the solution?