bytewax VS Flask

You can probably use Python to solve your problem, there are many ways you can speed up your deserialization/flattening. I work on Bytewax (https://github.com/bytewax/bytewax) and I wouldn't mention it if it wasn't a good fit, but I think it's worth looking at here. It is a stream processor that makes it easy to scale, maintain order, track progress, and you just write native Python.

Disclaimer: I work on Bytewax, but it feels like this could be a good fit and would save you some time looking around. If you need to do stateful operations (reduce, window, etc.) then you can use bytewax - https://github.com/bytewax/bytewax with pub/sub, but you would need to build a custom connector. There are some guides on how to do that - https://www.bytewax.io/blog/custom-input-connector.

This is great, definitely see the utility here. I have had to hack this together so many times while building streaming workflows with github.com/bytewax/bytewax and other tools.

When low latency matters you should always consider an ETL approach rather than ELT, e.g. collect data in Kafka and process using Kafka Streams/Flink in Java or Quix Streams/Bytewax in Python, then sink it to Snowflake where you can handle non-critical workloads (as is the case for 99% of BI/analytics). This way you can choose the right path for your data depending on how quickly it needs to be served.

Working on how to use https://github.com/bytewax/bytewax to create embeddings in real-time for ML use cases. I want to make a small library for embedding pipelines, but still learning about vector dbs and the tradeoffs between the different solutions.

Project looks cool! Glad you open sourced it. It could use some comments in the code base to help contributors ;). I also like the datafusion usage, that is awesome. BTW I work on github.com/bytewax/bytewax, which is based on https://github.com/TimelyDataflow/timely-dataflow another Rust dataflow computation engine.

Cool, nice idea. Can you sub in different backend like bytewax (https://github.com/bytewax/bytewax) for stateful processing?

If you want to keep in your Python/SQL area of expertise and by all means I don't mean to promote not learning a new language, but just as an FYI. There are some non-Java/Scala tools between streaming databases like risingwave and materialize, streaming platforms like fluvio and redpanda, and stream processors like bytewax and faust.

I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:

https://github.com/pallets/flask

#!/usr/bin/python # # https://flask.palletsprojects.com/en/3.0.x/installation/ # from flask import Flask, jsonify, request contacts = [ { "id": "1", "firstname": "Lorem", "lastname": "Ipsum", "email": "[email protected]", }, { "id": "2", "firstname": "Mauris", "lastname": "Quis", "email": "[email protected]", }, { "id": "3", "firstname": "Donec Purus", "lastname": "Purus", "email": "[email protected]", } ] app = Flask(__name__, static_url_path='', static_folder='public',) @app.route("/contact//save", methods=["PUT"]) def save_contact(id): data = request.json contacts[id - 1] = data return jsonify(contacts[id - 1]) @app.route("/contact/", methods=["GET"]) @app.route("/contact//edit", methods=["GET"]) def get_contact(id): return jsonify(contacts[id - 1]) @app.route('/') def root(): return app.send_static_file('index.html') if __name__ == '__main__': app.run(debug=True)

I was looking at all the big projects like numpy, pytorch, flask, etc.

Explore here.

I may use Flask-Admin initially to offload the "CRUD" operations to have an initial prototype fast but then drop it ASAP because I don't want to write a "flask-admin application" to fight against later on. If the application is mainly "CRUD", then Flask-Admin is suitable.

Now...

Would you do a breakdown/list of all the jobs you've done by sector/vertical and by function/role and by application functionality?

- [0]: https://flask.palletsprojects.com

- [1]: https://flask-admin.readthedocs.io/en/latest

- [2]: https://flask.palletsprojects.com/en/2.3.x/patterns/celery

- [3]: https://sentry.io

- [4]: https://posthog.com

- [5]: https://www.docker.com

In the lab to follow, we will be setting up an end-to-end DevOps workflow for a Flask microservice with GitHub Actions, using a self-managed custom runner for maximal control over the pipeline execution environment and automating deployments to a local Kubernetes cluster. Furthermore, we will construct separate pipelines for our "development" and "production" environments to further elaborate on the concepts of continuous deployment and delivery.

Flask Micro web Framework: You will use Flask to build a Flask web application.

In an earlier post I mentioned a bunch of Open Source web applications. Let's now focus on the ones written in Python using Flask the light-weight web framework.