At first glance, all serverless monitoring services seem similar and aim to solve the same problems. However, in Dashbird, we have made decisions that fundamentally differentiate us from our competitors since day one. Over time, those differences have magnified and we have found increasing confirmation and confidence in our approach. Dashbird product strategy is based on three core pillars.

AWS AppSync is a fully managed GraphQL service that makes it easy for you to build scalable and performant GraphQL APIs without having to manage any infrastructure! With AppSync, you get a lot of capabilities out of the box. Such as the ability to integrate directly with DynamoDB, ElasticSearch, Aurora Serverless, and Lambda. AppSync also supports both per-request as well as per-resolver caching and has built-in integration with CloudWatch and X-Ray.

Serverless platforms like AWS Lambda have helped accelerate application development by removing the need to provision and manage infrastructure resources. However, serverless architecture presents new monitoring challenges. Because AWS Lambda handles underlying infrastructure for you, you don’t have access to system-level metrics. Instead, you have to monitor your Lambda functions for insight into their performance and resource usage.

One problem that pops up quite frequently when people try to build serverless applications with AWS API Gateway and AWS Lambda is Execution failed due to configuration error: Malformed Lambda proxy response. There is nothing worse than generic error messages that don’t tell you anything you need to fix the problem, right? And AWS isn’t particularly known for its error message design, if you can even call it that, let alone for giving you the means of fixing the problem.

For quick, scalable, highly-available web services, few options compare to AWS Lambda. Just provide your code, add a little configuration, and you're done! In this article, Milap Neupane will introduce us to Lambda, show us how to get it working with Ruby and the Serverless Framework, and discuss reasons to use — or to not use! — Lambda in production.

With more and more businesses moving online, and homegrown entrepreneurs spinning up new online apps, they’re increasingly looking for an online development platform to help them easily build and deploy their sites.

Service maps are a visual and interactive depiction of how the services interact with each other. They also provide critical health information (such as latency, number of errors, etc.) for each service. As more and more applications are getting deployed as microservices, service maps have become increasingly important. In this blog, we will explore the importance of service maps in an observability solution and cover the basics of this feature in Epsagon.

In this article, we’ll be discussing everything you need to know about the basics of AWS Lambda error handling and some popular methods using StepFunctions and X-Ray. Regardless if you’re an AWS Lambda expert or if you’re a new Lambda user, there’s always something new to learn.

Among all the new features and services that AWS announced during the re:Invent 2020, my favorites were definitely the AWS Lambda updates. And there were many! For example, your code execution is no longer rounded up to the nearest 100ms of duration for billing — you are now billed on a per millisecond. On top of that, AWS increased the Lambda’s memory capacity to 10 GB, and correspondingly the CPU capacity up to 6 vCPUs.

Creating live and real-time systems is an important skill in the world of cross-platform integration, instant notifications, and real-time data. A key component of creating a real-time system is steaming data from one application to another. There are many great tools in the modern world that provide this ability, like Kafka, RabbitMQ, and Amazon Kinesis. All of these systems were developed with different goals and have their fair share of pros and cons. Today, we want to focus on Kinesis.