lightdash VS projects

> 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

You can look at the files in detail here. For this tutorial, I'll quickly mention a few crucial details.

Ploomber is the complete solution for notebook execution. It builds on top of papermill and extends it to allow writing multi-stage workflows where each task is a notebook. Meanwhile, it automatically manages orchestration. Hence you can run notebooks in parallel without having to write extra code.

The answer is YES, you can take advantage of OOP best practices to write good ETLs. For instance in this Ploomber sample ETL You can see there's a mix of .sql and .py files, it's within modular components so it's easier to test, deploy and execute. It's way easier than airflow since there's no infra work involved, you only have to setup your pipeline.yaml file. This also allows you to make the code WAY more maintainable and scalable, avoid redundant code and deploy faster :)

We have tons of examples that follow a standard layout, here’s one: https://github.com/ploomber/projects/tree/master/templates/ml-intermediate

I can talk about the open-source I'm working on Ploomber (https://github.com/ploomber/ploomber), it's focusing on seamless integration with Jupyter and IDEs. It allows an easy mechanism to orchestrate work for instance, here's an example SQL ETL and then you can deploy it anywhere, so if you're working with Airflow, it'll deploy it there too but without the complexity. You wouldn't have to maintain docker images etc.

I recommend using Ploomber which can help you build once and automate a lot of the work, and it works with python natively. It's open source so you can start with one of the examples, like the ML-basic example or the ETL one. It'll allow you to define the pipeline and then easily explain the flow with the DAG plot. Feel free to ask questions, I'm happy to help (I've built 100s of data pipelines over the years).

I'm not sure what pipeline frameworks support this kind of testing, but after successfully implementing this workflow, I added this feature to Ploomber, the project I'm working on. Here's how a pipeline looks like, and here's a tutorial.

Check out Ploomber, (disclaimer: I'm the author) it has a simple API, and you can export to Airflow, AWS, Kubernetes. Supports all databases that work with Python and you can seamlessly transfer from a SQL step to a Python step. Here's an example.

Without more specifics about your use case, it's hard to give more specific advice. But check out Ploomber (disclaimer: I'm the creator) - here's an example ETL pipeline. I've used it in past projects to develop Oracle ETL pipelines. Modularizing the analysis in many parts helps a lot with maintenance.

Yes! (tell your friend). You can write shell scripts so you can execute that 2002 code :) You can test it locally and then run it in AWS Batch/Argo. Here's an example