dbt-fal VS dbt-metabase

Compare dbt-fal vs dbt-metabase and see what are their differences.

dbt-fal

do more with dbt. dbt-fal helps you run Python alongside dbt, so you can send Slack alerts, detect anomalies and build machine learning models. (by fal-ai)
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dbt-fal dbt-metabase
12 1
851 431
- -
7.7 8.2
about 1 month ago 13 days ago
Python Python
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

dbt-fal

Posts with mentions or reviews of dbt-fal. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-26.

dbt-metabase

Posts with mentions or reviews of dbt-metabase. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-21.
  • A modern data stack for startups
    4 projects | dev.to | 21 Apr 2022
    So how do we get this into Metabase? There's a tool called dbt-metabase that can infer Metabase semantic type information from the dbt schema and push it into Metabase- we run this whenever complete a dbt build, helping sync Metabase with whatever new fields we may have added.

What are some alternatives?

When comparing dbt-fal and dbt-metabase you can also consider the following projects:

dbt-expectations - Port(ish) of Great Expectations to dbt test macros

dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

kuwala - Kuwala is the no-code data platform for BI analysts and engineers enabling you to build powerful analytics workflows. We are set out to bring state-of-the-art data engineering tools you love, such as Airbyte, dbt, or Great Expectations together in one intuitive interface built with React Flow. In addition we provide third-party data into data science models and products with a focus on geospatial data. Currently, the following data connectors are available worldwide: a) High-resolution demographics data b) Point of Interests from Open Street Map c) Google Popular Times

airflow-dbt - Apache Airflow integration for dbt

evidence - Business intelligence as code: build fast, interactive data visualizations in pure SQL and markdown

nodejs-bigquery - Node.js client for Google Cloud BigQuery: A fast, economical and fully-managed enterprise data warehouse for large-scale data analytics.

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

ngods-stocks - New Generation Opensource Data Stack Demo

pgsink - Logically replicate data out of Postgres into sinks (files, Google BigQuery, etc)

re_data - re_data - fix data issues before your users & CEO would discover them ๐Ÿ˜Š

MetabaseMonitoringToolkit - Set of queries designed to measure how users are consuming queries and dashboards.