fragments
lightdash
fragments | lightdash | |
---|---|---|
2 | 13 | |
14 | 3,419 | |
- | 2.3% | |
2.8 | 10.0 | |
10 months ago | 6 days ago | |
TypeScript | TypeScript | |
- | MIT License |
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.
fragments
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Fetch and transform data from a source and expose a REST API
I built this https://github.com/corpulent/fragments as an experiment/POC for fetching and transforming data from various sources and exposing it via a REST API for consumption in BI dashboards, apps, and so on. Jinja defines the structure a REST API should return, users can write custom functions to transform data. Data gets optionally cached after its processed the first time so consecutive API calls are instant. Jinja is optional, and users can just write Python code.
GraphQL is a similar idea to this.
I am burned out trying to figure out good use cases, a market to approach, and generally in what ways to expand on it further. So I am reaching out to the community in hopes of gaining some insights or ideas!
Thanks in advance.
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Sharing my data engineering project/idea, using jinja, docker, k8s, react UI.
Repo here https://github.com/corpulent/fragments
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