astro-sdk
typhoon-orchestrator
astro-sdk | typhoon-orchestrator | |
---|---|---|
7 | 14 | |
317 | 29 | |
0.9% | - | |
8.5 | 0.0 | |
5 days ago | over 1 year 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.
astro-sdk
-
Orchestration: Thoughts on Dagster, Airflow and Prefect?
Have you tried the Astro SDK? https://github.com/astronomer/astro-sdk
-
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).
-
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
-
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
-
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. 🤞
-
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!
-
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.
typhoon-orchestrator
- After Airflow. Where next for DE?
- New OSS Orchestrator - Where should we go next?
-
Airflow's Problem
I have my own opinion on Airflow's pain points and created Typhoon Orchestrator (https://github.com/typhoon-data-org/typhoon-orchestrator) to solve them. It doesn't have many stars yet but I've used it to create some pipelines for medium sized companies in a few days, and they've been running for over a year without issues.
In particular I transpile to Airflow code (can also deploy to Lambda) because I think it's still the most robust and well supported "runtime", I just don't think the developer experience is that good.
-
Data Engineering for very small businesses. Any experiences?
Typhoon Orchestrator This is a framework that I designed to help fix some of the pain points of Airflow so that I could build test and deploy pipelines faster. You could skip this step but if you want more info check here.
-
CSV data library to database
I am also collaborating on an open source tool called Typhoon Orchestrator (repo). It aims to make composing airflow data pipelines simple and quite quick. Putting pipeline steps together like lego.
-
Recommendations for simple ETL (Postgres to Snowflake)
The project (https://github.com/typhoon-data-org/typhoon-orchestrator) doesn't have many stars yet but I have deployed it on a medium sized hotel chain for several data sources with a similar use case to yours and it's been working for over a year with no intervention. If you decide to pursue this option I'd be willing to provide provide some support free of charge (feel free to PM me).
-
Impress your friends! Make a serverless bot that sends daily jokes to a Telegram Group
Typhoon Orchestrator is a great way to deploy ETL workflow on AWS Lambda. In this tutorial we intend to show how easy to use and versatile it is by deploying code to Lambda that gets a random joke from https://jokeapi.dev once a day and sends it to your telegram group.
-
My Thirty Years of Dodging Repetitive Work with Automation Tools
I think there's space for an open source library that can help with what you described. We originally created https://github.com/typhoon-data-org/typhoon-orchestrator to orchestrate ETL workflows, which would be a superset of the use cases you described. Our next goal is to allow deployment to AWS lambda which can be a good compromise between getting locked in with SAAS and hosting your own infrastructure.
Also check out Zappa's scheduled tasks that have a similar goal and inspired our library.
- Airflow, you complete me! Compose YAML DAGs for Airflow with auto-complete with Typhoon (Open Source).
- Use Airflow? Composable elegant YAML DAGS that transpile to Airflow. Zero risk and no migration.
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
JokeAPI - REST API that serves uniformly and well formatted jokes in JSON, XML, YAML or plain text format that also offers a great variety of filtering methods
quadratic - Quadratic | Data Science Spreadsheet with Python & SQL
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]
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."
pachyderm - Data-Centric Pipelines and Data Versioning
astronomer-cosmos - Run your dbt Core projects as Apache Airflow DAGs and Task Groups with a few lines of code
getting-started - This repository is a getting started guide to Singer.
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
jmespath.py - JMESPath is a query language for JSON.