Running async tasks is challenging by itself. Require continuous zero-downtime deployment, and it gets even harder.This talk presents battle-tested techniques and approaches to run your Celery workers on top of AWS ECS with Fargate while being able to deploy tens of times per day.This talk presents techniques, approaches, and practical configuration examples that allow you to run your Celery workers on top of AWS ECS with Fargate. It starts by presenting the problems you encounter when dealing with async tasks. Next, we'll take a look at ECS and its configuration examples - which parameters to tweak to achieve the best result. After that, we'll check Celery configuration examples that comply with ECS requirements. We'll finish by overviewing techniques for implementing tasks in a way that supports ECS + Celery configuration while allowing continuous deployment all the time.

Have you found that your code works beautifully on a few dozen examples, but leaves you wondering how to spend the next couple of hours after you start looping through all of your data? Are you only familiar with Python, and wish there was a way to speed things up without subjecting yourself to learning C?

In this talk, you'll see some simple tricks, borrowed from linear algebra, which can give you significant performance gains in your Python code, and how you can implement these in NumPy. We'll start exploring an inefficient implementation of an algorithm that relies heavily on loops and lists, and iteratively replace bottlenecks with NumPy vectorized operations.

At each stage, you'll learn the linear algebra behind why these operations are more efficient so that you'll be able to utilize these concepts in your own code. You'll see how straightforward it can be to make your code many times faster, all without losing readability or needing to understand complex coding concepts.

In the session, I will show how we can use Django to serve REST APIs.
- Create REST Endpoints quickly with DRF.
- Implement proper permissions on the REST endpoints, for example, an admin can perform any operation on any data. But a specific user can perform a limited operation on certain specific resources.
- Respond with different responses as per the defined user’s permission level. For example, the admin will get all the fields in the response, but a normal user will get only a selected field of the model.
- Implement Pagination on the REST endpoint.
- Create custom endpoints on the REST Endpoints.

Over a third of respondents to a StackOverflow survey professed to a dread of learning Apache Kafka. Nevertheless, with a curious mindset and the right resources, we have the tools to succeed in learning Kafka. Take the plunge with me; together, we will conquer concepts like events and topics, producers, and consumers.

We'll gain confidence through learning about partitions and brokers and how to use Kafka in the cloud. We'll go over the different configurations for producers and consumers, and how these configurations affect application behavior. Then, accompany me on a code walkthrough and see how we build and run producers and consumers in Python with the kafka-python client.

You'll be leaving with a spark of excitement, knowing you have neutralized your dread, and that you are now firmly within the two-thirds of StackOverflow respondents who are comfortable learning Apache Kafka.

For seasoned experts and beginners alike, developers must learn a different approach to building APIs with a different backend programming language.

With knowledge about using Node/Express, you can transfer the understanding from the runtime environment, Node.js, build your customised API, and remember testing the functionality with Postman.

In this talk, we'll go over the basic of Python and the strategies of using the Flask framework for building, testing, and possibly documenting your workflow with Postman.