Back in 2008 when we began our journey, cloud computing was still relatively new. Though the term had a lot of buzz around it, organizations only considered the cloud as a place to experiment, not as a viable alternative to run production workloads. Now, the cloud has become the go-to strategy for enterprises and startups alike to deliver their digital business.

As your AWS usage grows, so will your stockpile of EBS snapshots and AMI images. Many of these are used for backups, while others are used to launch EC2 instances from Auto Scaling groups and EC2 launch templates. As your collection of AMI images grows, you will need to start rotating out the old AMI images.

I’m going to be biased in this post and it’s going to be ok because all the information you’re about to see are 100% correct and fair towards both services.

Analyst firm IDC forecasts that organizations will collectively spend $370 billion on public cloud services and infrastructure in 2022. Given the skyrocketing adoption of public cloud platforms, enterprises are using a number of approaches (lift and shift, replatforming, and refactoring) to smoothly transition on-prem workloads to the cloud.

Alibaba Cloud provides a comprehensive suite of cloud computing services to power businesses across the globe. We are excited to announce that our new integration with Alibaba Cloud is now in public beta. While the Datadog Agent has always been able to provide visibility into Alibaba Cloud instances, this new integration now enables you to also monitor the health and performance of Alibaba Cloud services (load balancers, managed databases, and more) in Datadog.

Amazon DynamoDB is a key-value and document database that allows you to easily scale to huge numbers of records with single digit millisecond performance. However, since it’s a managed service, you have less visibility with traditional monitoring tools. As such, it becomes even more important to take advantage of the available monitoring tools in AWS. In this post, we’ll explain how to use CloudWatch to monitor DynamoDB and what is important to watch.

Over the years DevOps has well established its methodologies for performing continuous deployment of applications and patches with the least amount of effort. Moreover, it has successfully paced itself to be competitive in the face of digital disruption, for which many organizations are keen to adopt in all facets.

With AWS Lambda, we can deploy and scale individual functions. However, we as engineers still like to think in terms of services and maintain a mapping between business capabilities and service boundaries. The service level abstraction makes it easier for us to reason about large systems. As such, cohesive functions that work together to serve a business feature are grouped together and deployed as a unit (i.e. a service) through CloudFormation.

Don’t you just hate it when APIs are failing and you have absolutely no clue why? Now imagine you don’t have access to the VM, cluster or container where your software is running. Want me to continue with this nightmare? Yes, that’s what debugging AWS Lambda functions tends to seem like. A horrid nightmare of not knowing what is happening nor why things are failing. This article will show you a way of logging function invocations.

We recently released a new search key autocomplete feature here at Honeybadger. It was such a fun project that I just had to write it up for you all. Not only does it showcase an exciting use of DynamoDB, but it also shows the challenges of using Dynamo cost-effectively with large amounts of frequently-updated data.