Getting Started With Queryable Encryption in the MongoDB C# Driver

With the release of MongoDB 7.0 in August 2023, we introduced a feature called Queryable Encryption, the first of its kind. With queryable encryption, your data is encrypted, even at rest, with the server unable to read it either but still able to execute queries against it. You can specify what fields to encrypt so you can encrypt as much or as little of your document as you need.

The great news is, not only is this available for all tiers, but it is supported in our C# driver too!

In this tutorial, we are going to add queryable encryption to a healthcare application, ensuring that private information, such as social security number (SSN) and date of birth, is encrypted.

Building a Real-Time AI Fraud Detection System with Spring Kafka and MongoDB

In this tutorial, we’ll build a real-time fraud detection system using MongoDB Atlas Vector Search, Apache Kafka, and AI-generated embeddings. We’ll demonstrate how MongoDB Atlas Vector Search can be used to detect anomalies in a stream of financial transactions by analyzing a user’s transaction history and identifying suspicious behavior based on LLM-generated embeddings.

Our solution will monitor MongoDB Change Streams using the Java synchronous driver, triggering vector searches on each new transaction to detect potential fraud. While this approach works well for our demo, and for many use cases, we’ll also discuss its limitations. Throughout the tutorial, I’ll cover alternative strategies to optimize performance, whether you need higher transaction throughput, faster fraud detection, or a more scalable architecture.

Grab a Pint With Django-MongoDB-Backend, Voyage AI, and LangChain

DjangoCon Europe is just around the corner, and this year, it’s in Dublin, Ireland. This means two things:

1. I’m legally required to drink my first Guinness (yes, first ever!).
2. I obviously had to build an app to help me find the best pubs to accomplish my Guinness goal.

This full-stack, AI-powered Dublin City Center Pub Finder app is built using our new Django MongoDB Backend Python package, Voyage AI embeddings to best understand the queries, and LangChain to power our natural-language interface.

So, if you’re in Dublin and you really want a “Guinness, outside, with live music,” this Pub Finder will help you find the three best options!

Before we go into how it works and how you can build it yourself, let’s go over the platforms used.

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.

Local Development with the MongoDB Atlas CLI and Docker

Need a consistent development and deployment experience as developers work across teams and use different machines for their daily tasks? That is where Docker has you covered with containers. A common experience might include running a local version of MongoDB Community in a container and an application in another container. This strategy works for some organizations, but what if you want to leverage all the benefits that come with MongoDB Atlas in addition to a container strategy for your application development?

In this tutorial we’ll see how to create a MongoDB-compatible web application, bundle it into a container with Docker, and manage creation as well as destruction for MongoDB Atlas with the Atlas CLI during container deployment.