Data sources

In the previous section, we learned that a schema defines the shape of your data. However, we never explained where that data came from. In real projects, your schema is like a gateway that handles all requests made to the server. When a request is made, the schema acts as the single endpoint that interfaces with the client. The schema will access, process, and relay data from the data source back to the client. See the infographic below:

AWS AppSync and GraphQL superbly implement Backend For Frontend (BFF) solutions. They work in tandem to reduce complexity at scale by abstracting the backend. If your service uses different data sources and/or microservices, you can essentially abstract some of the complexity away by defining the shape of the data of each source (subgraph) in a single schema (supergraph). This means your GraphQL API is not limited to using one data source. You can associate any number of data sources with your GraphQL API and specify in your code how they will interact with the service.

As you can see in the infographic, the GraphQL schema contains all of the information clients need to request data. This means everything can be processed in a single request rather than multiple requests as is the case with REST. These requests go through the schema, which is the sole endpoint of the service. When requests are processed, a resolver (explained in the next section) executes its code to process the data from the relevant data source. When the response is returned, the subgraph tied to the data source will be populated with the data in the schema.

AWS AppSync supports many different data source types. In the table below, we'll describe each type, list some of the benefits of each, and provide useful links for additional context.

Data source	Description	Benefits	Supplemental information
Amazon DynamoDB	"Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB lets you offload the administrative burdens of operating and scaling a distributed database so that you don't have to worry about hardware provisioning, setup and configuration, replication, software patching, or cluster scaling. DynamoDB also offers encryption at rest, which eliminates the operational burden and complexity involved in protecting sensitive data."	Performance at scale: DynamoDB is designed around consistent performance at any scale. This is possible through the use of partitions. DynamoDB will automatically partition your tables into several allocations that will be stored in multiple SSDs across several nodes. This will generally increase network throughput and reduce latency. Capacity at scale: DynamoDB monitors your traffic and allows you to automatically scale your throughput if the network remains overloaded for extended periods of time. Availability and fault tolerance: DynamoDB is supported by several physically isolated Regions, each containing several physically isolated Availability Zones. DynamoDB will automatically switch to a backup zone in the event of a service disruption. You can also back up and replicate your data manually for data assurance. Logging and monitoring: DynamoDB provides several analytical tools for your tables. You can monitor your table's performance and create alarms to notify you of drastic changes to the service. Security: DynamoDB follows strict protocols to ensure your data complies with your organization's security requirements. Integration with AWS AppSync: DynamoDB is seamlessly integrated with our service. You can create new DynamoDB tables and automatically generate a schema from them to streamline your development process. We also provide an entire collection of operations to easily request data from existing DynamoDB tables in your account in your resolver.	DynamoDB official documentation Partitions Auto scaling Fault tolerance Monitoring Security GraphQL and DynamoDB Resolver operations for DynamoDB Pricing model
AWS Lambda	"AWS Lambda is a compute service that lets you run code without provisioning or managing servers. Lambda runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, and logging. With Lambda, all you need to do is supply your code in one of the language runtimes that Lambda supports."	Pay-as-you-use model: Lambda only charges you when you use their resources. They also allow you to scale the amount of resources used with your application needs. Automatic scaling: Sometimes your application may require extra computing power for a particular process. Lambda allows you to automatically scale computing resources to fit the needs of your application. Faster deployment times: You can streamline your development process via a deployment package. Use a package to upload your function code to the Lambda service. You can then use their runtime environments to test and execute your functions. Versatility: Lambda can be used in a multitude of use cases. You can seamlessly integrate Lambda with third-party services and AWS services alike. Some examples include CI/CD pipelines and mass mailing services. Integration with AWS AppSync: You can easily invoke your Lambda functions in your resolver to handle requests. Our service provides a streamlined request operation to perform Lambda calls. We allow both single and batched calls.	Official documentation Scaling deployment runtimes Lambda resolver tutorial Pricing model
OpenSearch	"Amazon OpenSearch Service is a managed service that makes it easy to deploy, operate, and scale OpenSearch clusters in the AWS Cloud. Amazon OpenSearch Service supports OpenSearch and legacy Elasticsearch OSS (up to 7.10, the final open-source version of the software). When you create a cluster, you have the option of which search engine to use. OpenSearch is a fully open-source search and analytics engine for use cases such as log analytics, real-time application monitoring, and clickstream analysis. For more information, see the OpenSearch documentation. Amazon OpenSearch Service provisions all the resources for your OpenSearch cluster and launches it. It also automatically detects and replaces failed OpenSearch Service nodes, reducing the overhead associated with self-managed infrastructures. You can scale your cluster with a single API call or a few clicks in the console."	Scaling: You can easily scale the service to fit your service requirements through OpenSearch Serverless. Data ingestion: You can use OpenSearch Ingestion to import, process, and analyze data. There are many applications for data ingestion, which you can find here. Security: OpenSearch can manage your AWS security configuration including IAM, CloudTrail, VPCs, authentication, etc. Availability: OpenSearch also supports different Regions and Availability Zones in its service. Integration with AWS AppSync: In AWS AppSync, you can use GraphQL APIs to store and retrieve data from existing OpenSearch Service domains in your account.	Official documentation Serverless Pricing model
HTTP endpoints	You can use HTTP endpoints as data sources. AWS AppSync can send requests to the endpoints with the relevant information like params and payload. The HTTP response will be exposed to the resolver, which will return the final response after it finishes its operation(s).	Useful for simple applications that aren't as integrated with services like Lambda.	Resolver reference
Amazon EventBridge	"EventBridge is a serverless service that uses events to connect application components together, making it easier for you to build scalable event-driven applications. Use it to route events from sources such as home-grown applications, AWS services, and third-party software to consumer applications across your organization. EventBridge provides a simple and consistent way to ingest, filter, transform, and deliver events so you can build new applications quickly."	Event-driven architecture: You can take advantage of event-driven architecture. Scheduling: You can use the EventBridge Scheduler to automate your tasks and rules using cron expressions or set time intervals as an alternative to event patterns. Pipes: Using EventBridge Pipes, you can replace the event bus with a pipe that includes additional filtering event patterns and enrichment through data transforms before sending the event to the target. Integration with AWS AppSync: AWS AppSync allows you to send events to event buses using your resolver.	Official documentation Pipes Scheduler Resolver reference Pricing model
Relational databases	"Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the AWS Cloud. It provides cost-efficient, resizable capacity for an industry-standard relational database and manages common database administration tasks."	Managing made easy: Periodically, RDS performs maintenance on its resources. Maintenance most often involves updates to the DB instance's underlying hardware, underlying operating system (OS), or database engine version. Under normal circumstances, you can decide when to perform updates (exceptions include security patches). Recommendations: RDS' recommendation feature provides automated suggestions for fixing potential issues in your instance. Availability: RDS is available in different physical Regions across the world. You can easily distribute your database needs across different nodes to provide better service to your customers. Customisation: RDS is tailored to meet the requirements of large corporations. RDS provides various options for computing, quick deployment, scalability, and storage. Security: RDS is integrated with several tools and services to maintain database security on the user, database, and network levels. Integration with AWS AppSync: If you're looking for a mature backend solution, AWS AppSync allows you to send, process, store, and return data using your instance as the data source.	Official documentation Features Maintenance Recommendations Storage options Availability Security Pricing model
None data source	If you aren't planning on using a data source service, you can set it to none. A none data source, while still explicitly categorized as a data source, isn't a storage medium. Despite that, it's still useful in certain instances for data manipulation and pass-throughs.	Potentially useful for things like data conversion Useful when resolving something locally	Resolver reference

Tip

For more information about how data sources interact with AWS AppSync, see Attaching a data source.