ibis VS splink

I really don't understand the appeal of dbt vs a proper programming language. The templating approach leads to massive spaghetti. I look forward to trying out something like Ibis [0]

0: https://ibis-project.org/

ibis – portable Python dataframe library

The main benefit is that you get a Python (or R, Julia or Rust) interpreter. So you can evaluate code. A good example of the value of this is the Ibis docs which use Quarto: https://ibis-project.org/

Ive found polars quite intuitive, though for python, I lean more towards [ibis](https://ibis-project.org/). The interface is nearly identical, but ibis has the benefit if building sql queries before pulling any actual data (like dbplyr) — whereas polars requires the data to be in-memory (at least for rdb’s, though correct me if Im wrong)

this to me seems like a good argument for only using ibis, but Im happy to be convinced otherwise

Please add Ibis Birdbrain https://ibis-project.github.io/ibis-birdbrain/ to the list. Birdbrain is an AI-powered data bot, built on Ibis and Marvin, supporting more than 18 database backends.

See https://github.com/ibis-project/ibis and https://ibis-project.org for more details.

https://github.com/moj-analytical-services/splink

Record linkage has been a big part of a project I've been working on for 6 months now. I personally think a great and free solution be using the splink package in Python which can handle 10+m rows which implements the Fellegi-Sunter model (equivalent to a naive-Bayes model) is the classical model in record linkage. It can be trained in an unsupervised manner using some initial parameter estimation (these are quite intuitive) and then expectation maximisation. The features in the model will be different pairwise string comparisons on your field of interest. These can include exact equality; edit distance comparisons like Levensthein distance and Jaro-Winkler; and phonetic comparisons like soundex and double metaphone. The splink pacakge will handle training the model and then all the graph theory at the end to connect all your links into clusters. All the details you'll need are in the links. https://www.robinlinacre.com/probabilistic\_linkage/ https://moj-analytical-services.github.io/splink/

https://moj-analytical-services.github.io/splink/ is a FOSS python package (but it runs against your db using SQL).

If you're curious, I've written a FOSS record linkage library that executes everything as SQL. It supports multiple SQL backends including DuckDB and Spark for scale, and runs faster than most competitors because it's able to leverage the speed of these backends: https://github.com/moj-analytical-services/splink

Splink - a python library for probabilistic record linkage (fuzzy matching/entity resolution).

Splink is dramatically faster and works on much larger datasets than other open source libraries. I'm particularly proud of the fact we support multiple execution backends (at the moment, DuckDb Spark Athena and Sqlite, but additional adaptors are relatively straightforward to write).

We've had >4 million pypi downloads and it's used in government, academia and the private sector, often replacing extremely expensive proprietary solutions.

https://github.com/moj-analytical-services/splink

More info in blog posts here:

Splink is a SQL tool that can do this https://github.com/moj-analytical-services/splink

Probabilistic record matching model such as a Fellegi-Sunter. Check out the splink package in Python.

Main docs here: https://moj-analytical-services.github.io/splink

It'd be great to see Splink add value in this area! Do give us a shout if you have any questions. The best place to post is on the Github discussions: https://github.com/moj-analytical-services/splink/discussions