Mete Atamel

Introduction

There are numerous scenarios where you might want to execute tasks in parallel. One common use case involves dividing data into batches, processing each batch in parallel, and combining the results in the end. This approach not only enhances the speed of the overall processing but it also allows for easier error detection in smaller tasks.

On the other hand, setting up parallel tasks, monitoring them, handling errors in each task, and combining the results in the end is not trivial. Thankfully, Google Cloud’s Workflows can help. In this post, we will explore how you can use a parent workflow to set up and execute parallel child workflows.

Introduction

In my previous couple posts (post1, post2), I shared my detailed notes on Generative AI Learning Path in Google Cloud’s Skills Boost. It’s a great collection of courses to get started in GenAI, especially on the theory underpinning GenAI.

Since then, I discovered another great resource to learn more about GenAI: Learn Generative AI Short Courses by DeepLearning.AI from Andrew Ng.

In this post, I summarize what each course teaches you to help you decide which course to take. I highly recommend taking all 4 courses. They’re full of useful information and short enough that even if you’re not fully interested in the topic, you can still get a good idea about it in a short amount of time.

Docker recently announced experimental support for running Wasm modules (see Announcing Docker+Wasm Technical Preview 2). In this blog post, I explain what this means and how to run a Wasm module in Docker.

Why run Wasm in a container?

In my Exploring WebAssembly outside the browser post, I mentioned how Wasm is faster, smaller, more secure, and more portable than a container. You might be wondering: Why take something faster, smaller, more secure, and more portable and run it in a container?

In my Exploring WebAssembly outside the browser post, I talked about how WebAssembly System Interface (WASI) enables Wasm modules to run outside the browser and interact with the host in a limited set of use cases that Wasi supports (see Wasi proposals).

In this blog post, let’s look into details of how to compile code to a Wasm+Wasi module and then run it in a Wasm runtime. Notice that I use Wasm+Wasi module deliberately (instead of just Wasm) because some languages have Wasm support and can run perfectly fine in the browser but they have no or limited Wasi support to run outside the browser.

Introduction

WebAssembly (Wasm) was initially designed as a binary instruction format for executing native code efficiently within web browsers. The original use cases are focused on augmenting Javascript in the browser to run native code in a fast, portable, and secure way for games, 3D graphics, etc.

However, its potential extends far beyond the browser. In this blog post, we’ll delve into the exciting realm of running Wasm outside the browser, exploring its advantages, and relevant specifications.

If you’re looking to upskill in Generative AI, there’s a Generative AI Learning Path in Google Cloud Skills Boost. It currently consists of 10 courses and provides a good foundation on the theory behind Generative AI.

As I went through these courses myself, I took notes, as I learn best when I write things down. In part 1 of the blog series, I shared my notes for courses 1 to 6. In this part 2 of the blog series, I continue sharing my notes for courses 7 to 10.

If you’re looking to upskill in Generative AI (GenAI), there’s a Generative AI Learning Path in Google Cloud Skills Boost. It currently consists of 10 courses and provides a good foundation on the theory behind Generative AI and what tools and services Google provides in GenAI. The best part is that it’s completely free!

As I went through these courses myself, I took notes, as I learn best when I write things down. In this part 1 of the blog series, I want to share my notes for courses 1 to 6, in case you want to quickly read summaries of these courses.

Workflows just released a new connector for Batch that greatly simplifies how to create and run Batch jobs from Workflows. Let’s take a look how you can use the new Batch connector of Workflows.

Recap: Batch and Workflows

Batch is a fully managed service to schedule, queue, and execute batch jobs on Google’s infrastructure. These batch jobs run on Compute Engine VM instances but they are managed by Batch service, so you don’t have to provision and manage VM instances yourself.

I’ve been covering different aspects of AsyncAPI in my recent blog posts. In this final post of my AsyncAPI blog post series, I want to talk about how to document Google Cloud’s Pub/Sub using AsyncAPI.

AsyncAPI has pretty good support for Google Pub/Sub, thanks to contributions from Jeremy Whitlock, an engineer from Google, and the flexibility baked in AsyncAPI spec. Jeremy also has a nice blog post on this topic that you can read for more details.

I’ve been recently talking about CloudEvents and AsyncAPI,two of my favorite open-source specifications for event-driven architectures. In this blog post, I want to talk about how you can use CloudEvents and AsyncAPI together. More specifically, I’ll show you how to document CloudEvents enabled services using AsyncAPI, thanks to the flexibility and openness of both projects.

Recap: CloudEvents and AsyncAPI

Let’s first do a quick recap CloudEvents and AsyncAPI.