cloudquery VS Typesense

There both do something like what you're looking for.... https://github.com/cloudquery/cloudquery https://github.com/openraven/magpie

Unified API is a holly grail but as many said quite difficult to abstract every use case in a scalable way that won't break. At CloudQuery (https://github.com/cloudquery/cloudquery) we focus solely on the ELT use-case(Founder/Maintainer here).

Congrats!! How does it compare to the ELT space and the modern data stack where you have ingestion/storage/visualization layers decoupled?

Asking as the founder of CloudQuery (https://github.com/cloudquery/cloudquery), Saw Datasette quite a few times around data exploration but curious to hear about the most popular use-cases of Datasette!

Congrats!! We also focus on performance at CloudQuery (https://github.com/cloudquery/cloudquery) by using Golang, gRPC and still trying to be abstract enough to support different databases :)

In any case good luck!

CloudQuery for ETL

Another ELT framework that's an alternative to Airbyte

Another alternate ELT

Cloudquery: https://cloudquery.io/

Hello! Im Yevgeny, Founder & maintainer at CloudQuery . We've built CloudQuery as an open source high performance ELT framework so you should get pretty good results syncing all your cloud assets from high number of accounts (we have users syncing more than 10K Azure subscription and thousands of AWS accounts concurrently).

CloudQuery supports a lot of APIs https://github.com/cloudquery/cloudquery

There are actually plenty of non-ES products that are way easier to integrate and tune (and get better results with less effort).

- Typesense (https://github.com/typesense/typesense)

- Algolia

- Google Programmable Search Engine (https://programmablesearchengine.google.com/about/)

Typesense - Open Source Alternative to Algolia

If you like your penny take a look at Typesense https://typesense.org/ - nothing to complain here. Especially nothing complain about pricing.

I work on Typesense [1] (historically considered an open source alternative to Algolia).

We then launched vector search in Jan 2023, and just last week we launched the ability to generate embeddings from within Typesense.

You'd just need to send JSON data, and Typesense can generate embeddings for your data using OpenAI, PaLM API, or built-in models like S-BERT, E-5, etc (running on a GPU if you prefer) [2]

You can then do a hybrid (keyword + semantic) search by just sending the search keywords to Typesense, and Typesense will automatically generate embeddings for you internally and return a ranked list of keyword results weaved with semantic results (using Rank Fusion).

You can also combine filtering, faceting, typo tolerance, etc - the things Typesense already had.

[1] https://github.com/typesense/typesense

[2] https://typesense.org/docs/0.25.0/api/vector-search.html

For something small with a minimal footprint, I'd recommend Typesense. https://github.com/typesense/typesense

Try tigris | typesense for faster search

I’m also checking out Typesense as a possibility for replacing Elastic: https://typesense.org/

Elasticsearch is heavy to run and manage. Take a look at Meilisearch and Typesense