Lambda Four Ways, a Rosetta Stone for AWS

When I write Lambdas professionally, Python is my preferred language. It offers decent performance, a straightforward syntax, and high developer productivity. I’ve also used Java, both in demonstration apps and actual client work. But while I have some familiarity with other languages supported by the platform, I’ve never used them. So, with some downtime, I decided to implement the same Lambda in four different languages: Python, Java, JavaScript, and Go, to get a better sense of their strengths and weaknesses.

Scaling Effortlessly: How Jenkins, Karpenter and EKS Redefines CI/CD

Jenkins has served as the backbone of the CI/CD landscape for over a decade. Throughout these years, CI/CD practices have transformed from jobs executed in companies’ own data centers to those running in the cloud. Jenkins has adapted and evolved throughout this time, remaining a workhorse in the ever-changing CI/CD domain. If you looked at a typical AWS-based Jenkins setup, you would probably see a master Jenkins node running in EC2. When initiating a job, the master node dynamically spawns…

Integrating Touch Support to Drag-and-Drop Interfaces

In the previous blog post, we went over how to use the HTML Drag-and-Drop interface, which is a well-supported web API that’s available to use across major web browsers. Here, we will go over how we can extend our previous demo to support touch interactions so that our application can be used across various devices but still provide the same functionality the user expects. For demonstration purposes I will be simulating a virtual mobile device using the Chrome Developer Tools…

Mastering the HTML Drag And Drop API

For web developers, creating intuitive and interactive user interfaces is essential to providing a great user experience. One feature that significantly enhances the user experience is drag-and-drop functionality. This feature allows users to manipulate elements on the screen in a natural and interactive way. In this article, we will explore the HTML Drag and Drop API and how to implement it in a web project. Why Drag and Drop? There are a multiple scenarios where a drag-and-drop like feature is…

Technology Trends in Gaming & Sports Betting 

One of the more remarkable turnarounds in recent American culture has been the embrace of sports betting and online gaming. What was once considered a vice and relegated to relatively few in-person jurisdictions has quickly exploded into the mainstream and online.   Like with any growth industry, this has had cascading consequences for others that work in the sector. This includes technology providers and partners that have had to rush to address the many challenges of such meteoric growth within a…

Perils of Partitioning

Partitioning is one of the easiest ways to improve the performance of your data lake, because it reduces the amount of data scanned. But implementing partitions can be surprisingly challenging, as can their effective use. In this post I look at several of the issues that you should consider when partitioning your data.

Large Language Model (LLM) Coding Assistance

With all the hype surrounding Generative AI/LLM, and all the hallucinations mentioned in the news, what are these actually good for? As it turns out LLMs trained for code generation are helpful. But what if you don’t want your code going to some cloud provider? The following is a great solution for that. Here is the plan: Install Ollama and load the model Install Continue Try it out Conclusion Install ollama and load the model Ollama allows you to run…

Transforming Data with Amazon Athena

My prior posts used Lambda to do data transformation. But what if we could use a non-programmatic tool, in keeping with the Extract-Load-Transform mindset of the modern data pipeline. As it turns, we can: Amazon Athena can write data as well as query it. There are, of course, a few stumbles along the way. In this blog post I walk through the process of aggregating CloudTrail data using SQL.

From RAGs to Riches – Adding Context to Your LLM

In my previous post, Experiences in Fine-Tuning LLMs: Time + Power = Potato?, I covered my experiences around trying to fine-tune an LLM (large language model) with a dataset, which gave me less than stellar results. Ultimately, fine-tuning is best for a use-case where additional reasoning & logic needs to be added to an LLM, but it’s subpar for adding information. However, if you’re trying to get an LLM to answer questions using your data, then retrieval augmented generation (RAG)…

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