sqlmesh
sayn
| sqlmesh | sayn | |
|---|---|---|
| 12 | 2 | |
| 1,317 | 117 | |
| 8.7% | 0.9% | |
| 9.9 | 6.8 | |
| about 13 hours ago | 9 days ago | |
| Python | Python | |
| Apache License 2.0 | 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.
sqlmesh
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Launch HN: Serra (YC S23) – Open-source, Python-based dbt alternative
There is also sqlmesh (https://sqlmesh.com/). Pretty new as well. It introduces some interesting concepts. For smaller dbt projects it could be a drop-in replacement as it allows importing dbt projects.
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DBT lays off 15% of their staff
I agree with you that they don't have a competitor yet. I think https://sqlmesh.com will be that competitor in the not too distant future though.
- SQL Mesh - Auto DAG generation!!
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Data transformation tools other than DBT
SQLMesh is a new SQL templating framework that addresses some of dbt's biggest gaps (column lineage, unit testing). It's not an enterprise solution, but it's an interesting project. https://github.com/TobikoData/sqlmesh
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Semantic Understanding of SQL
It’s a part of the SQLMesh IDE: https://github.com/TobikoData/sqlmesh
- Virtual Data Environments
- Blog Post on how DoorDash used the metrics layer to scale and standardize Metrics for Experimentation
- A dbt killer is born (SQLMesh)
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SQLMesh: The future of DataOps
If you don't plan on using Airflow, you can just add a custom connection implementation using one of the existing ones as a reference.
sayn
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Average reply times from some of my Facebook friends over the last few years [OC], full article here: https://medium.com/@timsugaipov/taking-your-facebook-messenger-data-further-f9da079b1409?source=friends_link&sk=3bd04bb35ad9a4b6f586300e52f96e4f
Data Processing: SAYN
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Introducing SAYN: A Simple Yet Powerful Data Processing Framework.
We believe simplicity to be crucial when maintaining pipelines at scale. However, we also believe that simplicity should not come at the expense of flexibility. This is why we have built our own open source data processing framework: SAYN. SAYN is designed to empower analytics teams by being simple, flexible and centralised. It democratises the contribution to data processes within an analytics team, enables full flexibility and helps save a lot of time through automation.
What are some alternatives?
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-databricks - A dbt adapter for Databricks.
dbd - dbd is a database prototyping tool that enables data analysts and engineers to quickly load and transform data in SQL databases.
dataform - Dataform is a framework for managing SQL based data operations in BigQuery
dbt-coves - CLI tool for dbt users to simplify creation of staging models (yml and sql) files
tinvois-parser - Extract receipt info
astro-sdk - Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow.
data-engineering-wiki - The best place to learn data engineering. Built and maintained by the data engineering community.
versatile-data-kit - One framework to develop, deploy and operate data workflows with Python and SQL.
beneath - Beneath is a serverless real-time data platform ⚡️
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]
yaetos - Write data & AI pipelines in (SQL, Spark, Pandas) and deploy to the cloud, simplified