dbt-coves
f1-data-pipeline
| dbt-coves | f1-data-pipeline | |
|---|---|---|
| 1 | 1 | |
| 209 | 23 | |
| 3.8% | - | |
| 9.2 | 6.8 | |
| 2 days ago | 11 months ago | |
| Python | Python | |
| Apache License 2.0 | - |
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.
dbt-coves
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Is there something wrong with me, I hate dbt, what am I missing ?
This just feels like you aren’t using the plentiful tools to make those “mind-numbingly slow” dev steps faster. For ex., using dbt-coves to generate the staging models with casting to types in a couple clicks. And pulling directly from Fivetran tables is just poor practice, with the additional steps needed to do it “right” being inconsequential at best.
f1-data-pipeline
What are some alternatives?
sqlmesh - Efficient data transformation and modeling framework that is backwards compatible with dbt.
dbt2looker - Generate lookml for views from dbt models
dbt-ml-preprocessing - A SQL port of python's scikit-learn preprocessing module, provided as cross-database dbt macros.
astro - Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow. [Moved to: https://github.com/astronomer/astro-sdk]
magic-the-gathering - A complete pipeline to pull data from Scryfall's "Magic: The Gathering"-API, via Prefect orchestration and dbt transformation.
steam-data-engineering - A data engineering project with Airflow, dbt, Terrafrom, GCP and much more!
datavault4dbt - Scalefree's dbt package for a Data Vault 2.0 implementation congruent to the original Data Vault 2.0 definition by Dan Linstedt including the Staging Area, DV2.0 main entities, PITs and Snapshot Tables.
weather_data_pipeline - This is a PySpark-based data pipeline that fetches weather data for a few cities, performs some basic processing and transformation on the data, and then writes the processed data to a Google Cloud Storage bucket and a BigQuery table.The data is then viewed in a looker dashboard
prefect-deployment-patterns - Code examples showing flow deployment to various types of infrastructure
astro-sdk - Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow.