Serverless Development with Kotlin, AWS Lambda, and MongoDB Atlas

As seen in a previous tutorial, creating a serverless function for AWS Lambda with Java and MongoDB isn’t too complicated of a task. In fact, you can get it done with around 35 lines of code!

However, maybe your stack doesn’t consist of Java, but instead Kotlin. What needs to be done to use Kotlin for AWS Lambda and MongoDB development? The good news is not much will be different!

In this tutorial, we’ll see how to create a simple AWS Lambda function. It will use Kotlin as the programming language and it will use the MongoDB Kotlin driver for interacting with MongoDB.

Serverless Development with AWS Lambda and MongoDB Atlas Using Java

So you need to build an application that will scale with demand and a database to scale with it? It might make sense to explore serverless functions, like those offered by AWS Lambda, and a cloud database like MongoDB Atlas.

Serverless functions are great because you can implement very specific logic in the form of a function and the infrastructure will scale automatically to meet the demand of your users. This will spare you from having to spend potentially large amounts of money on always on, but not always needed, infrastructure. Pair this with an elastically scalable database like MongoDB Atlas, and you’ve got an amazing thing in the works.

In this tutorial, we’re going to explore how to create a serverless function with AWS Lambda and MongoDB, but we’re going to focus on using Java, one of the available AWS Lambda runtimes.

Delivering a Near Real-Time Single View into Operations with a Federated Database

So the data within your organization spans across multiple databases, database platforms, and even storage types, but you need to bring it together and make sense of the data that’s dispersed. This is referred to as a Single View application and it is a common need for many organizations, so you’re not alone!

With MongoDB Data Federation, you can seamlessly query, transform, and aggregate your data from one or more locations, such as within a MongoDB database, AWS S3 buckets, and even HTTP API endpoints. In other words, with Data Federation, you can use the MongoDB Query API to work with your data even if it doesn’t exist within MongoDB.

What’s a scenario where this might make sense?

Let’s say you’re in the automotive or supply chain industries. You have customer data that might exist within MongoDB, but your parts vendors run their own businesses external to yours. However, there’s a need to pair the parts data with transactions for any particular customer. In this scenario, you might want to be able to create queries or views that bring each of these pieces together.

In this tutorial, we’re going to see how quick and easy it is to work with MongoDB Data Federation to create custom views that might aid your sales and marketing teams.

Interact with MongoDB in an AWS Lambda Function Using Go

If you’re a Go developer and you’re looking to go serverless, AWS Lambda is a solid choice that will get you up and running in no time. But what happens when you need to connect to your database? With serverless functions, also known as functions as a service (FaaS), you can never be sure about the uptime of your function or how it has chosen to scale automatically with demand. For this reason, concurrent connections to your database, which aren’t infinite, happen a little differently. In other words, we want to be efficient in how connections and interactions to the database are made.

In this tutorial we’ll see how to create a serverless function using the Go programming language and that function will connect to and query MongoDB Atlas in an efficient manner.