dbt-tidb VS ibis

Data build tool (dbt) is a popular open-source data transformation tool that enables analytics engineers to transform data in their warehouses through SQL statements. The TiDB community recently released the dbt-tidb adapter to make TiDB, a distributed SQL database to work with dbt. Through the dbt-tidb plug-in, analytics engineers working with TiDB can directly create forms and match data through SQL without having to think about the process of creating tables or views. They can also use Jinja, a dbt template language for writing SQL, test, package management, and other functions, which greatly improves efficiency.

Recently, we published a new release of dbt-tidb, an adapter that enables dbt to work with TiDB.

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.