sqlmesh
astro-sdk
sqlmesh | astro-sdk | |
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12 | 7 | |
1,296 | 319 | |
8.7% | 1.6% | |
9.9 | 8.5 | |
3 days ago | 8 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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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.
astro-sdk
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Orchestration: Thoughts on Dagster, Airflow and Prefect?
Have you tried the Astro SDK? https://github.com/astronomer/astro-sdk
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Airflow as near real time scheduler
One interesting point about putting the data into s3, is that if the data is in an S3 file then OP can use the Astro SDK to pretty easily upload that data into a table or a dataframe (there's even an s3 dynamic task function in the SDK that might fit the use-case well here).
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Most ideal Airflow task structure?
I think you should take a look at the Astro SDK It’s an open source python package that removes the complexity of writing DAGs , particularly in the context of Extract, Load, Transform (ELT) use cases. Look at the doc here, especially aql.transform, aql.run_raw_sql, etc. That will definitely help you
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ELT pipeline using airflow
- Astro SDK*: Made for folks who are doing their ETL in airflow and want to simplify movement between DBs and Pandas
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After Airflow. Where next for DE?
More of a general principle but when you don't have design patterns, you get varying levels of results right? I think what Astro is doing to introduce "strong defaults" through projects like the astro-sdk or the cloud ide are interesting experiments to remove some of the busy work of common dags (load from s3, do something, push to database) will HELP reduce the cognitive load of really common, simple actions and give them a better single pattern to optimize on. I don't think those efforts reduce the optionality of true power users at all who want to custom code their s3 log sink to have some unique implementation while at the same time maybe solving some of the fragmentation to very frequently performed operations. 🤞
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Airflow - Passing large data volumes between tasks
Have you looked into the astro python SDK? My team and I built this out over the last year to do exactly this :). You can you use the `@dataframe` decorator to pull the API data into a dataframe, store it in GCS and the access it in future steps. Lemme know if you have any questions!
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What's the best tool to build pipelines from REST APIs?
I have an example here using COVID data. basically you just write a python function that reads the API and returns a dataframe (or any number of dataframes) and downstream tasks can then read the output as either a dataframe or a SQL table.
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
dbd - dbd is a database prototyping tool that enables data analysts and engineers to quickly load and transform data in SQL databases.
quadratic - Quadratic | Data Science Spreadsheet with Python & SQL
dbt-coves - CLI tool for dbt users to simplify creation of staging models (yml and sql) files
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]
sayn - Data processing and modelling framework for automating tasks (incl. Python & SQL transformations).
starthinker - Reference framework for building data workflows provided by Google. Accelerates authentication, logging, scheduling, and deployment of solutions using GCP. To borrow a tagline.. "The framework for professionals with deadlines."
versatile-data-kit - One framework to develop, deploy and operate data workflows with Python and SQL.
astronomer-cosmos - Run your dbt Core projects as Apache Airflow DAGs and Task Groups with a few lines of code
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin