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The Ed-Fi Resources API, based on the Ed-Fi Data Standard, provides fine-grain access to educational data, modeled largely on the common denominators of the source systems that provide the data. For applications that consume data from an Ed-Fi API, this can result in a very “chatty” application integration: the consumer must make large numbers of calls over the network to retrieve the required data.

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Foundational Patterns

Direct Database

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Interaction

How it works

A dedicated backend application interacts directly with the Ed-FI database, bypassing the Ed-Fi API. If there is a user interface (UI), then it interfaces with that backend. In that case, the backend is likely an example of a Backend-for-Frontend (BFF). The BFF could be implementing either a REST or GraphQL interfaceFi database.

When to use

The Ed-Fi Alliance strongly discourages this pattern for the following reasons:

1. It bypasses the authorization security in the Ed-Fi API, potentially affecting both read and write operations.

2. This approach may put too much strain on the primary data storage, causing resource contention for other Ed-Fi client applications that are using the Ed-Fi API.

3. The Ed-Fi database structure is not a standard. Thus, different implementations or even different versions of the same implementation could have unstated breaking changes at the database layer. For example, the Ed-Fi ODS/API Platform and the Ed-Fi Data Management Service (unreleased at the time of writing) https://edfi.atlassian.net/wiki/spaces/ODSAPIS3V72 and the Data Management Service Platform have very different backend database structures. An integration built on the ODS/API’s EdFi_ODS database would not be compatible with the Data Management Service’s database.

Implementation Notes

Although no longer considered advisable, the performance benefits make this a tempting option, and indeed many . Many applications have been built on this model in the past. In such cases, it is advisable to limit the direct database interaction to read operations only, and to run from a read-only database copy. The copy could be a snapshot or replica. Using a read-only copy mitigates the resource contention concern on the primary database. Limiting to read operations eliminates half of the authorization security concern.

Also see Row-Level Authorization below.

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Real-time API Interaction

How it works

A dedicated backend application interacts directly with the Ed-Fi API, translating the incoming coarse-grained request into many fine-grained requests that utilize the Ed-Fi Resources API or other API specifications implemented in the Ed-Fi service application.

When to use

There is only a single front-end application that needs access to the Ed-Fi resources.

Implementation Notes

Also see Row-Level Authorization below.

Central Aggregating Gateway

How it works

When to use

There are multiple front-end applications with different use cases.

Implementation Notes

Also see Row-Level Authorization below.

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Define the problem:

• Course-grained access to fine-grained resources (“chattiness”)

  • Network latency

• Getting rid of composites

• Alternate security protocols

Solution: create a specialized backend API, which sits “close” to the Ed-Fi API (for low network latency).

• Central Aggregating Gateway: more generic design than a BFF, can support multiple user interfaces

• “A central-purpose aggregating gateway sits between external user interfaces and downstream microservices and performs call filtering and aggregation for all user interfaces. Without aggregation, a user interface may have to make multiple calls to fetch required information, often throwing away data that was retrieved but not needed.”

• BFF: designed for a single front-end

• “The main distinction between a BFF and a central aggregating gateway is that a BFF is single purpose in nature—it is developed for a specific user interface.”

• https://learning.oreilly.com/library/view/building-microservices-2nd/9781492034018/ch14.html by Sam Newman, ch 14, O’Reilly Media, Inc.

• Solves the impedance mismatch between systems.

• Similar to the Facade pattern in OO systems

Alternatives

• GraphQL - why not? Well-defined need is easier to express and reason about. Create a simple single definition of an API.

Design

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BEF app needs a key and secret to an ODS/API. Maybe to many of them.

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Analyze your authorization strategy and needs.

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Cache local data for higher performance.

• Possible implication: ETL

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Change Queries (ETL!)

• Show basic interaction

• Definitely(?) implies caching.

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Storing multiple tenants' data? Decide on a multi-tenancy pattern for segregating the data in the caching layer.

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Evolutionary diagrams:

• FE to BFF to BEF to ODS/API

• Add caching layer and ETL

Gateway Aggregation pattern

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Use when true “real-time” interaction with the Ed-Fi API is required. This pattern works best when only a small number of calls to the Ed-Fi API are needed or when the calling service can safely wait for an extended period of time. If many requests are required of the Ed-Fi API to fulfill the “user” application’s originating request, then there could be a substantial delay before responding. This might not be acceptable in a user interface application.

Caution: If this integration is intended to read data from the Ed-Fi API that were sourced from a different system, then real-time integration might not be feasible. Check to see if the other source system(s) have real-time or batched integrations. If batched, then help the end-users adjust their expectations about data freshness.

Implementation Notes

Ed-Fi API client credentials need to be managed directly in the backend application. The client_id and client_secret should be secured as strongly as one would secure credentials to a backend database.

Application performance may be improved by caching some data from the Ed-Fi API if real-time updates are not required for those cached data.

The Ed-Fi ODS/API Platform has a feature allowing API clients to use a read replica database. Using a read replica on GET requests can help reduce contention with systems that are actively writing to the API.

It may be useful to prepare for additional server load by monitoring resource consumption and performance and having a contingency plan for vertical scale-up (additional memory or CPU) and/or horizontal scale-out (additional nodes in clustered deployments).

Batch and Save

How it works

The backend application retrieves optimized data from a local data store, thus improving the response time on the originating request. A separate ETL process runs on a schedule to pull data from the Ed-Fi API, reshape it according to the backend application’s needs, and place into the shared data store.

When to use

Use when user interface responsiveness is more critical than data freshness, and/or when a single “front end” request would generate more than some small number (2? 3?) of synchronous calls to the Ed-Fi API.

This pattern is also advisable when further preparation is necessary before using the data – the “transform” portion of “ETL”.

Implementation Notes

API Credentials

Ed-Fi API client credentials will need to be managed in the ETL application. The client_id and client_secret should be secured as strongly as one would secure credentials to a backend database.

Scheduling

To optimize the batch scheduling, it may be useful to analyze the arrival time of data in the Ed-Fi API, potentially using queries on the backend database if it is accessible. If that database is not accessible, then try having a conversation with the service host to see if they can provide insight on the frequency and time of day when modifications are made in the Ed-Fi API.

Change Queries

If satisfying the frontend requirements only requires storing hundreds to thousands of records, it may be feasible to perform a full refresh of the data on a schedule. As the number of records to retrieve increases, a full refresh will take longer and can put significant strain on the Ed-Fi API. In such cases, the Change Queries API can be used to detect deleted records and to look for new or updated records.

Streaming Data

How it works

Ed-Fi Resources are copied into a streaming platform, such as Kafka, generally in real-time. Another application reads from the data streams, transforms data to fit the frontend application requirements, and saves the results into a local data store.

When to use

This pattern inverts the ETL process described in the Batch and Save pattern, by pushing changed records into the database instead of requiring a scheduled pull operation. It combines the “real-time” benefit of direct API integration with the data storage optimization of Batch and Save.

This architecture would be most appropriate when the end-user application is managed by the same organization that is managing the Ed-Fi API. Otherwise, it may be difficult to overcome the network and authorization security challenges between two different parties.

Caution: this pattern is not advisable when using the Ed-Fi ODS/API Platform. Technically feasible, it would require running Change Data Capture on the Ed-Fi ODS database. The result would be a data stream that looks like the ODS database, rather than looking like the Ed-Fi Unifying Data Model (as surfaced in the Ed-Fi API). Thus, this is in essence a more complex version of the Direct Database Interaction anti-pattern described above.

Other Ed-Fi API applications, such as the forthcoming Ed-Fi Data Management Service, or applications developed by parties other than the Ed-Fi Alliance, may support this pattern.

Implementation Notes

This is an emergent pattern that the Ed-Fi community has not widely used. It requires deployment of several additional components that are not present in other patterns (stream processor, change data capture, etc.).

Backend-for-frontend

How it works

This pattern starts from the needs of a specific user interface, creating a finely tuned API specification that optimizes data transfer for that application. The backend-for-frontend service (BFF) then handles translation of the custom specification into requests for data from a local data store or from the Ed-Fi API, following one of the Data Access Patterns above.

When to use

There is only a single front-end application that needs access to the Ed-Fi resources.

Implementation Notes

See Row Level Security below.

Also see: Backends for Frontends pattern - Azure Architecture Center | Microsoft Learn

Central Aggregating Gateway

How it works

Like the BFF pattern, this pattern creates a custom API that is more appropriate to the use case than the Ed-Fi API, aggregating what would otherwise be multiple calls to fetch data into a single (or fewer, at least) call to the gateway service. Unlike the BFF, the aggregating gateway is generalized to support multiple use cases or applications. It may even present a GraphQL interface instead of a REST interface.

When to use

When multiple applications need access to an Ed-Fi data, with strong overlap in the data required. If the required data sets are very different, then the optimization of a BFF service may be a better fit for purpose.

Implementation Notes

See Row Level Security below.

Also see: Gateway Aggregation pattern - Azure Architecture Center | Microsoft Learn

Special Case Patterns

ODS/API Composites

How it Works

The Ed-Fi ODS/API Platform host can deploy the system with API Composite Resources Composite API definition, such as the Enrollment API. These are specialized API definitions with auto-generated application code that generates complex database queries, allowing the client to make one call that will retrieve information from multiple Ed-Fi Resources. Depending on the API specification, this can effectively be a Backend-for-frontend, or a Central aggregating gateway (described below) - except that the authorization model is not appropriate for direct use by a user application.

When to Use

The Ed-Fi Alliance considers API Composite Resources to be a deprecated technology and recommends that no new integrations be built on this pattern.

API Publisher

How it Works

Rather than building a specialized API definition or directly calling the Ed-Fi API from a user application, the system provider instead utilizes API Publisher to copy some of the data from a remote Ed-Fi API instance into a local one. The local copy could be running the Ed-Fi ODS/API or another application that is a compatible Ed-Fi API. The host for the second copy now has full access to the Ed-Fi data in a local data store.

When to Use

This pattern may be useful when bridging across disparate networks and permissions. Although additional software needs to be deployed (extra copy of an Ed-Fi API, and the API Publisher), the only new software that needs to be developed is the backend or client application that serves end users. Arguably, this can be seen as a special case of the Batch and Save pattern above, without the need to develop a custom ETL application.

Implementation Notes

API Publisher’s configuration information allows the system administrator to select which resources to synchronize across systems. The application handles dependency ordering.

The local data store’s schema will depend on the which receiving application is used.

In some circumstances, this pattern can also be used to aggregate information from multiple Ed-Fi API deployments. However, this should only be done in a context where uniqueness of certain identifiers can be guaranteed. For example, when retrieving student data, the StudentUniqueId must be unique across all related source systems. This may be feasible when all of the source systems are in the same state, and the state education agency (SEA) has a strong state-wide unique identifier system in place. The EducationOrganizationId values (such as LocalEducationAgencyId and SchoolId) would also need to be unique across these source systems.

Specialized Ed-Fi Resources with Duplicate Data

How it Works

A new domain can be added to the Ed-Fi API, or a new entity can be added to an existing domain. The new entity/entities describe new shapes for data that already exist in one or more other domains in the Ed-Fi API. In effect, this creates a specialized API specification, which could be following either the backend-for-frontend or central aggregating gateway pattern. For example, the Enrollment API could be rebuilt as a new domain in the core Ed-Fi Data Standard.

A custom application would perform extract-transform-load (ETL) operations, copying data from the source resources in the Ed-Fi API into the target resources on the same application server or on another remote server.

When to Use

This pattern is ideal when the new resources benefit from the complex authorization security model in the Ed-Fi API. The new domain could either be an extension or part of the core Data Standard.

Implementation Notes

As with any ETL process, performance will be best when running against a database snapshot and/or running in “off hours”. This is particularly true when both sides of the ETL process are accessing the same instance of the Ed-Fi API.

Ideally, the ETL application would utilize the Change Queries API for reads, so that only newly modified data would need to be synced each day. However, this must be done very carefully: the point of this pattern is to collect information from multiple source endpoints to store in a single destination endpoint. Thus, the change version needs to be tracked across all sources for a given destination.

Row-Level Security

The Family Educational Rights and Privacy Act (FERPA) outlines certain data privacy rights for students plus the rules by which student data can be shared to anyone other than the student or parent. Systems that utilize Ed-Fi data must provide appropriate data security so that school officials, parents, and so forth are only authorized to view "need-to-know" records. What is appropriate may vary from state to state and district to district.

In K–12 scenarios, several common roles clearly require different degrees of authorization to view student data:

• Superintendents see data for all students in their district.

• Principals see data for all students in their school.

• Teachers see data for all students in their classes.

• Parents see data for all their children.

• Students see only data for themselves.

Real-world usage might not map job titles to data authorization levels in such a simple manner. There may be district employees other than superintendents who need access to all students. An Assistant Principal might take the lead on checking an Early Warning system. Rather than speaking about roles, it may be more useful to speak of access scopes, such as:

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• District

• School

• Section

Each application will need to devise its own mechanism for determining the correct scope of access for a user.