Recent Articles on the tecRacer AWS Blog

Building Lambda with terraform

Note: This is an updated version of this blog. Building Lambda Functions with Terraform Introduction Many of us use Terraform to manage our infrastructure as code. As AWS users, Lambda functions tend to be an important part of our infrastructure and its automation. Deploying - and especially building - Lambda functions with Terraform unfortunately isn’t as straightforward as I’d like. (To be fair: it’s very much debatable whether you should use Terraform for this purpose, but I’d like to do that - and if I didn’t, you wouldn’t get to read this article, so let’s continue)

Deploying a Serverless Dash App with AWS SAM and Lambda

Today I’m going to show you how to deploy a Dash app in a Lambda Function behind an API Gateway. This setup is truly serverless and allows you to only pay for infrastructure when there is traffic, which is an ideal deployment model for small (internal) applications. Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas.

Understanding Iterations in Ray RLlib

Recently I’ve been engaged in my first reinforcement learning project using Ray’s RLlib and Sagemaker. I had dabbled in machine learning before, but one of the nice things about this project is that it allows me to dive deep into something unfamiliar. Naturally, that results in some mistakes being made. Today I want to share a bit about my experience in trying to improve the iteration time for the IMPALA algorithm in Ray’s RLlib.

Streamlined Kafka Schema Evolution in AWS using MSK and the Glue Schema Registry

In today’s data-driven world, effective data management is crucial for organizations aiming to make well-informed, data-driven decisions. As the importance of data continues to grow, so does the significance of robust data management practices. This includes the processes of ingesting, storing, organizing, and maintaining the data generated and collected by an organization. Within the realm of data management, schema evolution stands out as one of the most critical aspects. Businesses evolve over time, leading to changes in data and, consequently, changes in corresponding schemas. Even though a schema may be initially defined for your data, evolving business requirements inevitably demand schema modifications. Yet, modifying data structures is no straightforward task, especially when dealing with distributed systems and teams. It’s essential that downstream consumers of the data can seamlessly adapt to new schemas. Coordinating these changes becomes a critical challenge to minimize downtime and prevent production issues. Neglecting robust data management and schema evolution strategies can result in service disruptions, breaking data pipelines, and incurring significant future costs. In the context of Apache Kafka, schema evolution is managed through a schema registry. As producers share data with consumers via Kafka, the schema is stored in this registry. The Schema Registry enhances the reliability, flexibility, and scalability of systems and applications by providing a standardized approach to manage and validate schemas used by both producers and consumers. This blog post will walk you through the steps of utilizing Amazon MSK in combination with AWS Glue Schema Registry and Terraform to build a cross-account streaming pipeline for Kafka, complete with built-in schema evolution. This approach provides a comprehensive solution to address your dynamic and evolving data requirements.