trino_data_mesh VS lightdash

Not central to the main ideas of this article, but if you want to have a data mesh that is self-service, why force folks to use a particular storage medium like a data warehouse? That still requires centralization of the data.

Why not instead have a tool like Trino (https://trino.io) that allows you to let different domains use whatever datastore they happen to use. You still would need to enforce schema, but this can be done in tools like schema registry as mentioned in the article along with a data cataloging tool.

These tools facilitate the distributed nature of the problem nicely and encourage healthy standards to be discussed and the formalized in schema definitions and catalogs that remove the ambiguity of discourse and documentation.

Nice example is laid out in this repo of how Trino can accomplish data mesh principles 1 and 3 (https://github.com/findinpath/trino_data_mesh).

> YAML, pivoting being done in the frontend, no symmetric aggregates

(one of the maintainers of Lightdash) You touched on some of our most interesting problems here! Would be especially interested to hear about what you liked / didn't like about symmetric aggregates in Looker and how you find dev with YAML. If you have an idea of how you'd like these to look in Lightdash, the team would be really open to making that a reality.

For pivoting in the backend, this is coming! Issue here: https://github.com/lightdash/lightdash/issues/2907

What are the 5 hottest dbt Repositories one should star on Github 2022?

dbt is a software framework that sits in the middle of the ELT process. It represents the transformative layer after loading data from an original source. Dbt combines SQL with software engineering principles.

Here are my top5!

- Lightdash (https://github.com/lightdash/lightdash): Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface.

- ⏎ re_data (https://github.com/re-data/re-data): Re-Data is an abstraction layer that helps users monitor dbt projects and their underlying data. For example, you get alerts when a test failed or a data anomaly occurs in a dbt project.

- evidence (https://github.com/evidence-dev/evidence): Evidence is another tool for lightweight BI reporting. With Evidence, you can build simple reports in "medium style" using SQL queries and Markdown.

- Kuwala (https://github.com/kuwala-io/kuwala): With Kuwala, a BI analyst can intuitively build advanced data workflows using a drag-drop interface on top of the modern data stack without coding. Behind the Scenes, the dbt models are generated so that a more experienced engineer can customize the pipelines at any time.

- fal ai (https://github.com/fal-ai/fal): Fal helps to run Python scripts directly from the dbt project. For example, you can load dbt models directly into the Python context which helps to apply Data Science libraries like SKlearn and Prophet in the dbt models.

Lightdash ( https://github.com/lightdash/lightdash ) Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface. The front end helps to understand and extend the underlying SQL queries. Lightdash also visualizes business metrics and makes them shareable with the data team. It is also possible to integrate all data into another visualization tool.

- ⚡️ Lightdash: Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface.

Visualization / Analysis: Lightdash, Superset

Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset

1) An integration with Metabase Cloud is on our roadmap for Q1! We'd love to integrate with Lightdash, but they don't have a public API just yet[1].

2) Several of our customers use us to alert on schema changes in Postgres, specifically so they can get ahead of application database changes that will end up in the warehouse, so you're definitely not alone! Here's a link on how to connect postgres: https://docs.metaplane.dev/docs/postgres

That's an excellent stack and one we kept front and center when building out Metaplane, so definitely let us know if you have any feedback or suggestions here!

[1]: https://github.com/lightdash/lightdash/issues/632

I would recommend lightdash which is essentially an open source Looker clone https://github.com/lightdash/lightdash