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Recently (on July 7, 2023), I was involved in a discussion around this topic along with other panelists. This was part of a larger event around Empowering Developers to Build Faster and Innovate More.
Since I think it's worth sharing some insights, I wanted to write a post to review our discussion. It's a topic close to my heart (developer enablement) and it's a good change-up from the usual stuff I usually write about.
In this blog post, you will learn how to build a Serverless speech-to-text conversion solution using Amazon Transcribe, AWS Lambda, and the Go programming language. Audio files uploaded to Amazon Simple Storage Service (S3) will trigger a Lambda function which will submit an asynchronous job to Amazon Transcribe (using the AWS Go SDK) which will, in turn, store the result in another S3 bucket.
You will be using the Go programming language for the business logic (thanks to the aws-lambda-go library) as well as the infrastructure component (Go bindings for AWS CDK) to deploy the solution.
In this blog post, you will learn how to build a Serverless solution to process images using Amazon Rekognition, AWS Lambda and the Go programming language. Images uploaded to Amazon Simple Storage Service (S3) will trigger a Lambda function which will detect labels (using the AWS Go SDK) and persist the image label data to an Amazon DynamoDB table.
You will be using the Go programming language for the business logic (thanks to the AWS-lambda-go library) as well as the infrastructure component (Go bindings for AWS CDK) to deploy the solution.
In this blog post, you will learn how to build a serverless solution for invoice processing using Amazon Textract, AWS Lambda, and the Go programming language. Invoices and expense receipt images uploaded to Amazon S3 will trigger a Lambda function which will extract invoice metadata (ID, date, amount, etc.) using the AWS Go SDK and persist it to an Amazon DynamoDB table. You will also use Go bindings for AWS CDK to implement "Infrastructure-as-code" for the entire solution and deploy it with the AWS Cloud Development Kit (CDK) CLI.
The code is available on GitHub.
Want to learn how to auto-scale your Kinesis Data Streams consumer applications on Kubernetes so you can save on costs and improve resource efficiency? This blog offers a step-by-step guide on how to do just that.
By leveraging Kubernetes for auto-scaling Kinesis consumer applications, you can benefit from its built-in features such as the Horizontal Pod Autoscaler.
In this blog post, you will be using the aws-lambda-go library along with the AWS Go SDK v2 for an application that will process records from an Amazon SNS topic and store them in a DynamoDB table.
You will also learn how to use Go bindings for AWS CDK to implement “Infrastructure-as-code” for the entire solution and deploy it with the AWS CDK CLI.
I recently presented this talk at the Conf42 Golang 2023 and I thought it might be a good idea to turn it into a blog post for folks who don't want to spend 40+ mins watching the talk (it's ok, I understand) or just staring at slides trying to imagine what I was saying.
So, here you go!
The field of machine learning has advanced considerably in recent years, enabling us to tackle complex problems with greater ease and accuracy. However, the process of building and training machine learning models can be a daunting task, requiring significant investments of time, resources, and expertise. This can pose a challenge for many individuals and organizations looking to leverage machine learning to drive innovation and growth.
That's where pre-trained AI Services come in and allow users to leverage the power of machine learning without having extensive machine learning expertise and needing to build models from scratch - thereby making it more accessible for a wider audience. AWS machine learning services provide ready-made intelligence for your applications and workflows and easily integrate with your applications. And, if you add Serverless to the mix, well, that's just icing on the cake, because now you can build scalable and cost-effective solutions without having to worry about the backend infrastructure.
In this blog post, you will be using AWS Controllers for Kubernetes on an Amazon EKS cluster to put together a solution wherein data from an Amazon SQS queue is processed by an AWS Lambda function and persisted to a DynamoDB table.
AWS Controllers for Kubernetes (also known as ACK) leverage Kubernetes Custom Resource and Custom Resource Definitions and give you the ability to manage and use AWS services directly from Kubernetes without needing to define resources outside of the cluster. The idea behind ACK is to enable Kubernetes users to describe the desired state of AWS resources using the Kubernetes API and configuration language. ACK will then take care of provisioning and managing the AWS resources to match the desired state. This is achieved by using Service controllers that are responsible for managing the lifecycle of a particular AWS service. Each ACK service controller is packaged into a separate container image that is published in a public repository corresponding to an individual ACK service controller.
In this blog post, you will learn how to run a Go application to AWS App Runner using the Go platform runtime. You will start with an existing Go application on GitHub and deploy it to AWS App Runner. The application is based on the URL shortener application (with some changes) that persists data in DynamoDB.
AWS App Runner is a robust and user-friendly service that simplifies the deployment process of web applications in the AWS Cloud. It offers developers an effortless and efficient way to deploy their source code or container image directly to a scalable and secure web application without requiring them to learn new technologies or choose the appropriate compute service.
A previous post covered how to deploy a Go Lambda function and trigger it in response to events sent to a topic in an MSK Serverless cluster.
This blog will take it a notch further.
In this post, you will learn how to deploy a Go Lambda function and trigger it in response to events sent to a topic in an MSK Serverless cluster.
The following topics have been covered:
The DynamoDB Scan API accesses every item in a table (or secondary index). It is the equivalent of a select * from query. One of the things I will cover in this blog is how to use Scan API with the DynamoDB Go SDK.
To scan a table, we need some data to begin with! So in the process, I will also go into how to use the Batch API to write bulk data in DynamoDB. You can use the BatchWriteItem API to create or delete items in batches (of twenty-five) and it's possible to you can combine these operations across multiple tables.
DynamoDB provides a rich set of data types including Strings, Numbers, Sets, Lists, Maps etc. In the Go SDK for DynamoDB, the types package contains Go representations of these data types and the attributevalue module provides functions to work with Go and DynamoDB types.
This blog post will demonstrate how to handle conversions between Go types in your application and DynamoDB. We will start off with simple code snippets to introduce some of the API constructs and wrap up with an example of how to use these Go SDK features in the context of a complete application (including a code walk though).
In an earlier blog post you saw how to use cdk8s with AWS Controllers for Kubernetes (also known as ACK), thanks to the fact that you can import existing Kubernetes Custom Resource Definitions using cdk8s! This made it possible to deploy DynamoDB along with a client application, by using cdk8s and Kubernetes.
But, what if you continue using AWS CDK for AWS infrastructure and harness the power cdk8s (and cdk8s-plus!) to define Kubernetes resources using regular code? Thanks to the native integration between the AWS EKS module and cdk8s, you can have the best of both worlds!
AWS re:Invent 2022 is right around the corner, and as always, it will offer plenty of learning opportunities for developers, engineers, and others who want to build new and innovative things on AWS. First held in 2012, AWS re:Invent is the company’s annual learning conference for the global cloud computing community. Scheduled to take place in Las Vegas, Nevada, from Monday, November 28, to Friday, December 2, it will feature over 1500 sessions.
If you are joining us at AWS re:Invent 2022, then the BuildOn.AWS (BOA) track is definitely something to look out for! The sessions on this track are specially curated for builders with a focus on how to build real-world solutions, deep dive into the architecture and code.
AWS Controllers for Kubernetes (also known as ACK) is built around the Kubernetes extension concepts of Custom Resource and Custom Resource Definitions. You can use ACK to define and use AWS services directly from Kubernetes. This helps you take advantage of managed AWS services for your Kubernetes applications without needing to define resources outside of the cluster.
Say you need to use an AWS S3 Bucket in your application that’s deployed to Kubernetes. Instead of using AWS console, AWS CLI, AWS CloudFormation etc., you can define the AWS S3 Bucket in a YAML manifest file and deploy it using familiar tools such as kubectl. The end goal is to allow users (Software Engineers, DevOps engineers, operators etc.) to use the same interface (Kubernetes API in this case) to describe and manage AWS services along with native Kubernetes resources such as Deployment, Service etc.
It's hard to operate stateful distributed systems at scale and Redis is no exception. Managed databases make life easier by taking on much of the heavy lifting. But you still need a sound architecture and apply best practices both on the server (Redis) as well as the client (application).
This blog covers a range of Redis-related best practices, tips and tricks including cluster scalability, client-side configuration, integration, metrics etc. Although I will be citing Amazon MemoryDB and ElastiCache for Redis from time to time, most (if not all) will be applicable to Redis clusters in general.