# Integration architectures
<a name="architecture"></a>

MongoDB Atlas integrates seamlessly with most AWS services, as shown in the following diagram.

![\[Integration between MongoDB Atlas and AWS services, by category.\]](http://docs.aws.amazon.com/prescriptive-guidance/latest/migration-mongodb-atlas/images/integration-architecture.png)


The following sections describe reference architectures to integrate MongoDB Atlas on AWS with AWS AppSync, Amazon SageMaker AI, Amazon EventBridge, Amazon Data Firehose, and Amazon Managed Streaming for Apache Kafka (Amazon MSK). All these reference architectures are built on a secured network by using AWS PrivateLink, AWS KMS, and IAM roles. For more information, see the [Best practices section](best-practices.md) later in this guide.

**Topics**
+ [

# Streamlined data integration with AWS AppSync
](data-integration.md)
+ [

# Generative AI with Amazon SageMaker AI JumpStart and MongoDB Atlas Vector Search
](generative-ai.md)
+ [

# Event-driven architecture with Amazon EventBridge
](event-driven.md)
+ [

# Data streaming with Amazon Data Firehose
](data-streaming.md)
+ [

# Real-time processing with Amazon MSK
](real-time-processing.md)
+ [

# Fraud detection with Amazon SageMaker AI Canvas
](fraud-detection.md)

# Streamlined data integration with AWS AppSync
<a name="data-integration"></a>

Integrating MongoDB Atlas with [AWS AppSync](https://aws.amazon.com/pm/appsync/) provides seamless data synchronization, real-time interactions, and dynamic, responsive user experiences. The following diagram shows an example implementation. 

![\[Integrating MongoDB Atlas with AWS AppSync for data synchronization.\]](http://docs.aws.amazon.com/prescriptive-guidance/latest/migration-mongodb-atlas/images/data-integration.png)


Key highlights:
+ A unified GraphQL endpoint for multiple data sources
+ Sub-graphs managed independently
+ End-to-end serverless architecture
+ Conflict resolution by using schema directives
+ Automatic scaling based on API request volumes

For more information, see the blog post [How to Build Advanced GraphQL-based APIs With MongoDB Atlas and AWS AppSync Merged APIs](https://www.mongodb.com/blog/post/how-build-advanced-graphql-based-apis-mongodb-atlas-aws-appsync-merged-apis) on the MongoDB website.

# Generative AI with Amazon SageMaker AI JumpStart and MongoDB Atlas Vector Search
<a name="generative-ai"></a>

[Amazon SageMaker AIJumpStart](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html) provides pre-trained AI foundation models such as Retrieval Augmented Generation (RAG) for intelligent text applications. You can combine JumpStart with [MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search), which enables semantic similarity queries on text, image, and other data, to build powerful search experiences. For example, your developers can implement intuitive semantic search over customer conversations by using Atlas Vector Search, and use Amazon SageMaker AI RAG models to add interactive summarization and translation, as illustrated in the following diagram. 

![\[Integrating MongoDB Atlas with Amazon SageMaker AI, for generative AI capabilities.\]](http://docs.aws.amazon.com/prescriptive-guidance/latest/migration-mongodb-atlas/images/gen-ai.png)


This unlocks a variety of AI-driven search use cases, including automated support, smart content management, content summarization, and enhanced recommendations. By implementing intuitive precision search with MongoDB and generative capabilities from Amazon SageMaker JumpStart, developers can rapidly deliver impactful cognitive search applications. 

Key highlights:
+ Enterprise chatbot use cases
+ Support for the RAG model architecture
+ MongoDB Atlas Vector Search
+ Support for 2K Embedding
+ Secured data transfer
+ Reduced likelihood of hallucinations

For more information about this implementation, see the AWS blog post [Retrieval-Augmented Generation with LangChain, Amazon SageMaker AI JumpStart, and MongoDB Atlas Semantic Search](https://aws.amazon.com/blogs/machine-learning/retrieval-augmented-generation-with-langchain-amazon-sagemaker-jumpstart-and-mongodb-atlas-semantic-search/).

# Event-driven architecture with Amazon EventBridge
<a name="event-driven"></a>

You can integrate MongoDB Atlas with [Amazon EventBridge](https://aws.amazon.com/eventbridge/) to orchestrate data flows, enable automated responses, and gain near real-time insights for applications. The following diagram shows an example reference architecture. 

![\[Integrating MongoDB Atlas with Amazon EventBridge to implement an event-driven architecture.\]](http://docs.aws.amazon.com/prescriptive-guidance/latest/migration-mongodb-atlas/images/event-driven.png)


Key highlights:
+ Seamless event orchestration
+ Real-time responsiveness
+ Automated workflows
+ Scalability and agility
+ Insights for Innovation

For more information about this implementation, see the AWS blog post [Ingesting MongoDB Atlas data using Amazon EventBridge](https://aws.amazon.com/blogs/compute/ingesting-mongodb-atlas-data-using-amazon-eventbridge/).

# Data streaming with Amazon Data Firehose
<a name="data-streaming"></a>

You can integrate MongoDB Atlas with [Amazon Data Firehose](https://aws.amazon.com/kinesis/data-firehose/) to stream, transform, and load data efficiently. This integration provides automated, real-time data delivery and scalability for optimized analytics and insights. The following diagram shows an example reference architecture. 

![\[Integrating MongoDB Atlas with with Amazon Data Firehose, to implement data streaming features.\]](http://docs.aws.amazon.com/prescriptive-guidance/latest/migration-mongodb-atlas/images/data-streaming.png)


Key highlights:
+ Dynamic schema evolution
+ Continuous data streaming
+ Enhanced analytics
+ Scalability and agility
+ Reliable data delivery

For more information, see the AWS blog post [Integrating MongoDB's Application Data Platform with Amazon Data Firehose](https://aws.amazon.com/blogs/big-data/integrating-the-mongodb-cloud-with-amazon-kinesis-data-firehose/).

# Real-time processing with Amazon MSK
<a name="real-time-processing"></a>

You can integrate MongoDB Atlas with [Amazon Managed Streaming for Apache Kafka (Amazon MSK)](https://aws.amazon.com/msk/) enhances real-time data processing. You can build robust, event-driven architectures by using the streaming capabilities in Amazon MSK with the MongoDB document model for agile and data-rich applications. The following diagram illustrates an example reference architecture. 

![\[Integrating MongoDB Atlas with Amazon MSK, to improve real-time data processing.\]](http://docs.aws.amazon.com/prescriptive-guidance/latest/migration-mongodb-atlas/images/real-time-processing.png)


Key highlights:
+ Seamless event integration
+ Event-driven agility
+ Real-time insights
+ Application-driven analytics
+ Highly scalable data streams

For details and step-by-step implementation instructions, see the AWS blog post [Build a serverless streaming pipeline with Amazon EMR Serverless, Amazon MSK Connect, and MongoDB Atlas](https://aws.amazon.com/blogs/big-data/build-a-serverless-streaming-pipeline-with-amazon-msk-serverless-amazon-msk-connect-and-mongodb-atlas/).

# Fraud detection with Amazon SageMaker AI Canvas
<a name="fraud-detection"></a>

You can integrate MongoDB Atlas with [Amazon SageMaker AI Canvas](https://docs.aws.amazon.com/sagemaker/latest/dg/canvas.html) to construct a powerful fraud detection system that combines real-time data analysis with advanced machine learning to help detect and prevent fraudulent activity.

The following diagram shows an example reference architecture for detecting fraud. 

![\[Integrating MongoDB Atlas with Amazon SageMaker AI Canvas, to implement fraud detection.\]](http://docs.aws.amazon.com/prescriptive-guidance/latest/migration-mongodb-atlas/images/fraud-detection.png)


(The diagram was adapted with permission from the [MongoDB website](https://www.mongodb.com/resources/products/unmasking-deception-harnessing-power-atlas-amazon-sage-maker-canvas-fraud-detection).)

For more information, see the MongoDB blog post [Unmasking Deception: Harnessing the Power of MongoDB Atlas and Amazon SageMaker AI Canvas for Fraud Detection](https://www.mongodb.com/resources/products/unmasking-deception-harnessing-power-atlas-amazon-sage-maker-canvas-fraud-detection).