Skytrax-Data-Warehouse
dagster
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| Skytrax-Data-Warehouse | dagster | |
|---|---|---|
| 1 | 46 | |
| 131 | 10,114 | |
| - | 4.3% | |
| 0.0 | 10.0 | |
| about 4 years ago | 7 days ago | |
| Python | Python | |
| MIT License | 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.
Skytrax-Data-Warehouse
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Open source contributions for a Data Engineer?
Always open to accept contributions to my project (Skytrax Data Warehouse). If you are into data stuff support my work at youtube as well (One Developer Pirate), I mostly make data-oriented videos. These days I'm making a SQL course from a data analysis perspective that is expected to release in next week.
dagster
- Experience with Dagster.io?
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Dagster tutorials
My recommendation is to continue on with the tutorial, then look at one of the larger example projects especially the ones named “project_”, and you should understand most of it. Of what you don't understand and you're curious about, look into the relevant concept page for the functions in the docs.
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The Dagster Master Plan
I found this example that helped me - https://github.com/dagster-io/dagster/tree/master/examples/project_fully_featured/project_fully_featured
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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The Why and How of Dagster User Code Deployment Automation
In Helm terms: there are 2 charts, namely the system: dagster/dagster (values.yaml), and the user code: dagster/dagster-user-deployments (values.yaml). Note that you have to set dagster-user-deployments.enabled: true in the dagster/dagster values-yaml to enable this.
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Best Orchestration Tool to run dbt projects?
Dagster seemed really cool when I looked into it as an alternative to airflow. I especially like the software defined assets and built-in lineage which I haven't seen in any other tool. However it seems it does not support RBAC which is a pretty big issue if you want a self-service type of architecture, see https://github.com/dagster-io/dagster/issues/2219. It does seem like it's available in their hosted version, but I wanted to run it myself on k8s.
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dbt Cloud Alternatives?
Dagster? https://dagster.io
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What's the best thing/library you learned this year ?
One that I haven't seen on here yet: dagster
- Anyone have an example of a project where a handful of the more popular Python tools are used? (E.g. airbyte, airflow, dbt, and pandas)
- Can we take a moment to appreciate how much of dataengineering is open source?
What are some alternatives?
dbd - dbd is a database prototyping tool that enables data analysts and engineers to quickly load and transform data in SQL databases.
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
jaydebeapi - JayDeBeApi module allows you to connect from Python code to databases using Java JDBC. It provides a Python DB-API v2.0 to that database.
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
dbt-spotify-analytics - Containerized end-to-end analytics of Spotify data using Python, dbt, Postgres, and Metabase
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
airflow-api-tests - This is a collection of Pytest for the 2.0 Stable Rest Apis for Apache Airflow. I have another repo where you could setup airflow locally and play around with these. I am used to RestAssured, but trying out pytest here.
MLflow - Open source platform for the machine learning lifecycle
DataGristle - Tough and flexible tools for data analysis, transformation, validation and movement.
meltano