Welcome to Chapter 2 of our walkthrough of “Learn Data Structures and Algorithms with Golang” by Bhagvan Kommadi. This is Part 1, where we cover the Go-specific building blocks you need before getting into the heavier data structure topics. Think of this as your toolkit chapter.
In Part 1 we covered what data structures are, how they get classified, and a bunch of structural design patterns. Now we’re getting to the fun stuff: algorithms, how to measure their performance, and a few classic algorithm types.
Chapter 1 of the book jumps right into classifying data structures and showing some structural design patterns in Go. There’s a lot packed in here, so I’m splitting it into two parts. This first part covers what data structures are, how they’re classified, and a bunch of design patterns with Go code.
So I picked up this book called “Learn Data Structures and Algorithms with Golang” by Bhagvan Kommadi (Packt Publishing, 2019, ISBN: 978-1-78961-850-1). And I’m going to retell the whole thing here on the blog, chapter by chapter.
This is post 17 in my Data Engineering with AWS retelling series.
You have a data lake. Terabytes of files sitting in S3 across landing zones, clean zones, and transform zones. The data is there. But how do you actually ask it questions? You could spin up a database, load everything into it, and then query. But that defeats the purpose of having a data lake in the first place.
This is post 16 in my Data Engineering with AWS retelling series.
Up to this point in the book, we have been doing everything by hand. Click this button in the console. Run that Glue job manually. Trigger a crawler. Upload a file. It works fine for learning. But imagine doing that in production, every day, at 3 AM, across dozens of data sources. No thanks.
Previous: What’s Next for Blockchain? The Research Frontier
And that’s a wrap! We’ve spent the last 12 days diving deep into Blockchain and Banking by Pierluigi Martino. This book really changed how I think about the money in my pocket (and on my screen).
Previous: The Future of Banking: My Final Take
We’ve covered a lot of ground this week. But as Pierluigi Martino points out in the final chapter of Blockchain and Banking, we’re still just at the beginning. Most of what we “know” about blockchain in banking is still just theory.
We have reached the end of our deep dive into Big Data on Kubernetes by Neylson Crepalde. It has been a massive journey, moving from basic Docker containers to complex, real-time AI pipelines.
We have built some incredible pipelines over the last few posts. But if you were to take what we’ve built and put it into production today, you’d quickly realize that there is a lot more to managing a platform than just getting the YAML files right.