We recently released the Lumigo Dynamic Dashboard to help our customers visualize metrics to monitor their environment the way they want.

With the volume, velocity, and variety of today’s data, we have all started to acknowledge that there is no one-size-fits-all database for all data needs. Instead, many companies shifted towards choosing the right data store for a specific use case or project. The distribution of data across different data stores brought the challenge of consolidating data for analytics.

Microservices and serverless are both important topics in the world of cloud-native computing. Yet, although serverless functions and microservices architectures often go hand-in-hand, they’re distinct technologies that fill different roles in modern software environments. Here’s an overview of what microservices and serverless are, how they relate to each other, how they are different, and why you may or may not wish to deploy a serverless microservice.

Cloud-native and serverless come hand in hand. One of the initial motivations to move business workflows to the cloud was related to cutting costs related to provisioning infrastructure and elasticity that on-demand allocation of resources is offering.

I’ve learned a lot about serverless in my first full year at Stackery after joining from the Ops-focused automation company, Puppet. I’ve learned how to deploy rich CMS-backed web applications in a serverless way, how to cast incantations using CloudFormation’s intrinsic functions, but most of all I’ve learned that there’s a lot to learn in order to be successful in AWS.

Mutual of Enumclaw, a 100-year-old insurance company from Washington, modernized their policy processing system in order to reduce TCO and focus on their software and customers - not infrastructure. However, they realized serverless development still presents many DevOps challenges like environments, workflows, documentation and local development.

Last year, we released native tracing for AWS Lambda through Datadog APM to provide deep visibility into serverless functions and surface performance issues such as cold starts and errors, without any added latency. But Lambda functions are only one piece of the puzzle in a rapidly growing serverless ecosystem, which includes message queues, data streams, notification services, and more.

When using services created by other people, it’s often neither obvious what they mean, let alone how to fix them. One of these error messages you might see when using Amazon API Gateway is “encoding not enabled”. The first question here is, what kind of encoding does this error message refer to? The first thought might go into the video or audio encoding direction and lead to a dead-end since you probably didn’t send any audio or video files.

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. It is great if you want to create a cost-effective, on-demand service. You can use it as part of a bigger project where you have multiple services or as a standalone service to do a certain task like controlling Alexa Skill.

How do we get started on monitoring AWS Lambda? Let me first introduce you to the term serverless computing. It doesn't matter if you have been in the tech industry only a few months, or you started writing code when Pascal was still considered cutting edge, you probably would have heard the term serverless computing thrown around in recent times. But what exactly is serverless computing?