astro-sdk
jq
astro-sdk | jq | |
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7 | 306 | |
317 | 25,063 | |
0.9% | - | |
8.5 | 0.0 | |
6 days ago | 11 months ago | |
Python | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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.
jq
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GNU Parallel, where have you been all my life?
That should recursively list directories, counting only the files within each, and output² jsonl that can be further mangled within the shell². You could just as easily populate an associative array for further work, or $whatever. Unlike bash, zsh has reasonable behaviour around quoting and whitespace too.
¹ https://zsh.sourceforge.io/Doc/Release/User-Contributions.ht...
² https://github.com/jpmens/jo
³ https://github.com/stedolan/jq
- How do i edit reputation?
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Jj: JSON Stream Editor
What I miss from jq and what is implemented but unreleased is platform independent line delimiters.
jq on Windows produces \r\n terminated lines which can be annoying when used with Cygwin / MSYS2 / WSL. The '--binary' option to not convert line delimiters is one of those pending improvements.
https://github.com/stedolan/jq/commit/0dab2b18d73e561f511801...
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Building and deploying a web API powered by ChatGPT
If you have jq installed you can use it to make the output look nicer.
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Search in your Jupyter notebooks from the CLI, fast.
It requires jq for JSON processing and GNU parallel for concurrent searches in the notebooks.
- Check the jq manual!
- mkv vs mp4 metadata
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Amazon Begs Employees Not to Leak Corporate Secrets to ChatGPT
jq is your friend.
- Memes are all cool and all. But this is your daily remaining that 10000! =
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How to export/import/externally-edit/whatever WI entries?
The jq command (https://stedolan.github.io/jq/) is useful pulling that information out.
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
yq - Command-line YAML, XML, TOML processor - jq wrapper for YAML/XML/TOML documents
quadratic - Quadratic | Data Science Spreadsheet with Python & SQL
dasel - Select, put and delete data from JSON, TOML, YAML, XML and CSV files with a single tool. Supports conversion between formats and can be used as a Go package.
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]
gojq - Pure Go implementation of jq
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."
json5 - JSON5 — JSON for Humans
astronomer-cosmos - Run your dbt Core projects as Apache Airflow DAGs and Task Groups with a few lines of code
jp - Validate and transform JSON with Bash
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
nushell - A new type of shell