About 90% of all Lambda functions monitored by Dashbird on AWS Lambda are running Nodejs and Python runtimes. Is this purely a reflection of the general popularity of these programming languages?

From the development and operations side, launching a new software application can be quite challenging. Deciding which tools to use, how to organize the task pipeline, managing collaboration among team members, monitoring performance and potential issues after launch, etc. It’s not easy to get it done right. Dashbird recently went through all of this. Behind the scenes, our amazing development team worked really hard to overcome all challenges and deliver the best value to our users.

TL;DR: yes, API Gateway can replace what a Load Balancer would usually provide, with a simpler interface and many more features on top of it. The downside is that it doesn’t come cheap. Load balancers have been one of the most common ways to expose a backend API to the public or even to an internal/private audience. API Gateways seem to provide the same functionality: map and connect HTTP requests to a backend service. So, are they the same or are there any differences?

Cost-efficiency is one of the main pillars of the Serverless Well-Architected framework. Read-intensive applications can save money and improve efficiency by using cache systems. AWS Lambda’s internal memory could be used as a caching mechanism. A Lambda container remains alive after an invocation is served, even if it stays idle for some time. Whatever was loaded in the container’s memory will remain there for the next invocations.

The most common integration type for AWS API Gateway is with Lambda functions. The API service can integrate with virtually any other service that accepts HTTP requests, though. This opens up possibilities to use the API Gateway as a proxy to database queries, without any compute layer such as a Lambda function. The direct integration between API and database is perfect when Lambda serves only as an intermediator.

Dashbird, centralised observability and incident detection platform for serverless applications. Team Dashbird is proud to announce that we have achieved the AWS Lambda Ready designation, part of the Amazon Web Services (AWS) Service Ready Program. This designation recognizes that Dashbird’s serverless monitoring and debugging platform has demonstrated successful integration with AWS Lambda.

Both DynamoDB and MongoDB are NoSQL databases, but the similarities probably end there. In this article, we cover their strengths and weaknesses in 8 basic categories, so that you can decide which one suits best your needs. While the data model behind Mongo is more flexible for storage and retrieval, Dynamo is stronger in terms of scalability, consistent performance under heavy load, and infrastructure abstraction.

We recently wrote about the reasons why serverless apps fail and explored some ideas to make architectures more resilient and scalable. Some of these architectural designs can become expensive if we don’t consider the financial impacts of architectural decisions. With proper care and consideration to this aspect, it is possible to achieve the same value in terms of scalability and resiliency while keeping costs at a manageable level.

There is an interesting discussion going on around how Serverless is more of a spectrum rather than a binary choice. The move towards the Serverless-end of the cloud spectrum builds upon a decades-old trend, which is why Serverless is here to stay.