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.
analytics
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I'm not getting it...what's the point of DBT?
Take a look at gitlab's dbt project: https://gitlab.com/gitlab-data/analytics/-/blob/master/transform/snowflake-dbt/models/common/schema.yml
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How would you structure a repo with 10+ ETL pipelines and shared code?
A good reference is the Gitlab data team repo. https://gitlab.com/gitlab-data/analytics
- What are your favourite GitHub repos that shows how data engineering should be done?
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Are there any open corporate Data Team repositories / projects besides GitLab?
For example, their Data Team have a public repository, with a bunch of information on how they organize DAGs, machine learning projects, system configuration, etc.
- Kimball Dim Modelling Code Examples
- Can someone help me, an absolute newbie, understand the usage and benefit of dbt with practical example ?
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Is jinja templating right for DBT?
So I've run through the DBT tutorial stuff and looked over some fairly complex uses of it i.e. GitLab Data and I was wondering if anyone has any opinions or insights into the use of jinja templating in the sql?
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Where can I find free data engineering ( big data) projects online?
Gitlab has their DBT repo open source and is very useful for seeing how to structure a project at scale. https://gitlab.com/gitlab-data/analytics/-/tree/master/transform/snowflake-dbt
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Gitlab's Data Team Platform (in depth look at their stack)
Currently the team is working hard on this: https://gitlab.com/gitlab-data/analytics/-/issues/9508
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Can someone explain the big deal with dbt?
GitLab's dbt project is an excellent example of a mature project at scale. They also have a comprehensive guide to their methodology.
lightdash
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Apache Superset
> 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
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What are the 5 hottest dbt Repositories one should star on GitHub 2022?
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.
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What are the hottest dbt Repositories you should star on Github 2022? - Here are mine.
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.
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What are your hottest dbt repositories in 2022 so far? Here are mine!
- ⚡️ Lightdash: Lightdash converts dbt models and makes it possible to define and easily visualize additional metrics via a visual interface.
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Data pipeline suggestions
Visualization / Analysis: Lightdash, Superset
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Where can I find free data engineering ( big data) projects online?
Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset
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Launch HN: Metaplane (YC W20) – Datadog for Data
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
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what's your experience with Looker ?
I would recommend lightdash which is essentially an open source Looker clone https://github.com/lightdash/lightdash
- a full semantic model based on dbt, dimensions, joins and metrics
- An open source alternative to Looker built using dbt. Made for analysts
What are some alternatives?
dbt-synapse - dbt adapter for Azure Synapse Dedicated SQL Pools
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
dagster - An orchestration platform for the development, production, and observation of data assets.
superset - Apache Superset is a Data Visualization and Data Exploration Platform
castled - Castled is an open source reverse ETL solution that helps you to periodically sync the data in your db/warehouse into sales, marketing, support or custom apps without any help from engineering teams
Rakam - 📈 Collect customer event data from your apps. (Note that this project only includes the API collector, not the visualization platform)
datahub - The Metadata Platform for your Data Stack
trino_data_mesh - Proof of concept on how to gain insights with Trino across different databases from a distributed data mesh
AdvancedSQLPuzzles - Welcome to my GitHub repository. I hope you enjoy solving these puzzles as much as I have enjoyed creating them.
elementary - The dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
dbt-unit-testing - This dbt package contains macros to support unit testing that can be (re)used across dbt projects.
streamlit - Streamlit — A faster way to build and share data apps.