astro-sdk
memphis
astro-sdk | memphis | |
---|---|---|
7 | 52 | |
317 | 3,149 | |
0.9% | 0.7% | |
8.5 | 9.9 | |
5 days ago | 2 months ago | |
Python | Go | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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.
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.
memphis
- Memphis
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What type of open source contributions can I make that improve my core data engineering skills? Are there any projects that require help of that nature? What are they?
Hey check out the first good issues in the Memphis.dev open source
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I want to create beginners Data Pipeline with SQL, Python etc. Any expert suggestions on like (Tools, Processes, Sources).
Try Memphis.dev blog or you can check out Github
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What's an ideal project structure for a Golang web service?
- https://github.com/memphisdev/memphis
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Connect Memphis as an Argo event source
Argo is a collection of open-source tools for Kubernetes to run workflows, manage clusters, and do GitOps easily. Memphis is an open-source next-generation alternative to traditional message brokers.
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Creating a brand new data infrastructure for a small company
I have dealt with the same problem. It depends what are the cycle intervals. If for example, it's every few minutes maybe it's worth keeping a machine on the cloud and the DB on that machine. in my opinion, it's a little bit expensive. The thing that worked for me is to run once a day a lambda function and store the data on a message broker. You should take a look at memphis.dev which is open-source and very easy to work with.
- I’m looking for a suggestion for a queuing library
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Memphis: Low-code real-time data processing platform
Image Source
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Memphis.dev v0.4.2
Join our 2K stargazers on Github and try Memphis out, I am sure you are going to be surprised 📷 https://github.com/memphisdev/memphis-broker
- Memphis.dev v0.2.4 is 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
gRPC - The C based gRPC (C++, Python, Ruby, Objective-C, PHP, C#)
quadratic - Quadratic | Data Science Spreadsheet with Python & SQL
ApacheKafka - A curated re-sources list for awesome Apache Kafka
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]
Nodejs-Developer-Roadmap - A Developer Roadmap to becoming a Node.js developer in 2019
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."
kubernetes - Production-Grade Container Scheduling and Management
astronomer-cosmos - Run your dbt Core projects as Apache Airflow DAGs and Task Groups with a few lines of code
compression - Node.js compression middleware
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
v8.dev - The source code of v8.dev, the official website of the V8 project.