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WorkOS
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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 demograp
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.
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.
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.
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.