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Scope each customer integration around a small shared contract: standardize the common data, transport, and security behavior; make reusable differences configurable or composable; and isolate unavoidable customer-specific logic in a contained connector. Before choosing technology, define the outcome, data flow, performance constraints, failure behavior, and the team that will own it.
Start with the job this integration must do
Write a one-sentence outcome, such as “Show a support agent current order status from the customer’s order system.” Then name the systems involved, who owns the data, which direction it moves, and which component makes each decision. Is the product reading remote data, sending a command, exchanging events, or synchronizing stored records?
Scope each integration point separately, even when two points connect the same pair of systems. A user’s request to look up a record and a nightly transfer of those records have different triggers, latency expectations, and failure modes. Salesforce Architects frames one remote-data use case as viewing, searching, and modifying data stored outside Salesforce without moving it into Salesforce; that is one valid pattern, not a universal answer for every integration request.
Record constraints before selecting a pattern
Capture the workflow conditions that determine whether an approach will work. Do not promise an interactive response when the source system, network, or data volume cannot deliver one.
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- Freshness and latency: How current must the result be, and how long can a user or downstream process wait?
- Volume and payload: Estimate requests or records per period, payload size, and whether work can be divided into batches.
- Trigger and window: Is work initiated by a user, a source-system change, a schedule, or a process milestone? For batch work, what is the permitted window?
- Connectivity and identity: Which network routes, endpoints, and authentication methods can both sides support?
- Data and access constraints: Are there residency, privacy, or authorization boundaries that constrain where data can be read or stored?
- Operations: Who monitors the connection, investigates errors, and communicates with the customer?
Salesforce Architects’ integration pattern guidance distinguishes small-volume, real-time work from large-volume synchronization and identifies timeliness, volume, endpoint capabilities, and error handling as design factors.
Choose a pattern that matches the workflow
These options address different jobs. Select based on the constraints you recorded rather than treating any one pattern as the default.
On-demand request and response
Use this when a user action should retrieve or update information and continuous copying is unnecessary. Make status and failure visible to the user, and set a timeout that fits the source system’s capabilities. This keeps a live lookup from quietly becoming a synchronization project.
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Event-driven or message-based processing
Use events or messages when a change should trigger processing and the systems should not need to complete work in one tightly coupled call. Queues can buffer work and help isolate availability or load differences. Microsoft’s basic enterprise integration architecture shows queues and events alongside APIs and workflow components as building blocks, not as a universal prescription.
Synchronization
Synchronize when separate stores must hold aligned data for a business, performance, or regulatory reason. Specify the direction of each flow, which side wins conflicts, how progress is tracked (for example, with a watermark), and how to recover after a missed or failed run. Two-way synchronization needs explicit conflict rules; “keep both systems in sync” is not a complete specification.
Batch transfer
Batching suits work that can tolerate a scheduled window or involves substantial volume. Agree on the window and protect source and target systems from contention. A batch process and a small real-time call have different endpoint, volume, and error-handling constraints, as Salesforce’s integration guidance makes clear.
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Define a shared contract, then isolate differences
Set the common rules before building customer variations: canonical data representations, error formats, supported transports, authentication boundaries, versioning expectations, and ownership of mapping changes. Keeping formats consistent across customers reduces the number of transformations and compatibility paths the product team must carry. Microsoft’s guidance on tenant integration and data access cautions that tenant-specific code creates additional paths that are harder to test and modify.
Not every customer can use the same schema or connectivity. Put such differences at a clear boundary: a customer connector can translate an external format into the shared representation, after which the shared process handles the common work. Keep mapping and customer-specific decisions out of core domain logic where possible. Salesforce Architects’ architecture patterns recommends explicit interfaces, reusable components, configuration-driven behavior, and separation of integration concerns from core domain logic.
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For each special case, decide whether it is a reusable variation, a genuinely different connector, or a requirement that serves only one customer. Reusable differences may fit configuration or a composable processing step. A distinct external system or protocol may justify a separate connector. A one-customer rule should not become an implicit permanent branch in the shared path.
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When customer-specific logic is necessary, contain it behind an adapter or anti-corruption boundary: translate the customer’s inputs and outputs there, and keep the shared process independent. Before accepting the exception, record who pays for its implementation and ongoing maintenance, which paths need regression tests, how upgrades affect it, who supports it, and when it can be retired.
Microsoft’s Power Platform integration pattern guidance describes modular, purpose-built flows and warns that monolithic flows or excessive centralization can create maintenance challenges. The same principle applies to customer integrations: avoid both a single flow that owns every customer’s logic and a proliferating set of nearly identical customer forks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare the real trade-offs
Use the following questions to compare viable designs. The right-hand column is a decision prompt, not a claim that one side is always better.
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| Decision axis | What to establish |
|---|---|
| Real-time or batch | Does the job need immediate freshness, or can it run within an agreed batch window? |
| Request/response or event/message | Must the caller receive the result in the same interaction, or can processing continue asynchronously? |
| Copy or federated access | Must records be stored in the product, or can users access remote data when needed? |
| Standard or customer-specific schema | Can the customer map to the shared contract, or is a bounded translation connector required? |
| Shared connector or isolated adapter | Can the same connector serve multiple tenants through configuration, or is a separate boundary warranted? |
| Synchronous or decoupled failure behavior | Can work wait in a queue, or does the caller depend on an immediate downstream response? |
| Operational ownership | Who owns schema changes, onboarding, connector health, incident response, and deprecation? |
| Total lifecycle burden | Compare implementation and ongoing support effort, including regression paths and upgrades, not just initial build scope. |
Design security and recovery into the boundary
Document who may call each API, how identity is verified, how requests are bounded, what is logged, and where secrets are stored. An API gateway can centralize policies such as authentication and request handling, but customers should not receive direct credentials to primary data stores. Microsoft’s Azure reference architecture illustrates API management, workflow/connectors, authentication, secret handling, and messaging as possible components.
For synchronous calls, specify timeout and retry behavior and consider circuit breakers and bulkheads so a failing dependency does not cascade into the rest of the product. Retries must account for duplicate delivery: define whether operations are idempotent or how duplicates are detected. For asynchronous work, define what happens to a message that repeatedly fails, how it is surfaced for investigation, and how it can be safely replayed. Resilience is part of the integration contract, not an afterthought to add when a customer reports an outage.
Assign lifecycle ownership before launch
A design is not complete until teams know who operates it. Name owners for the shared schema and versioning, connector health, customer onboarding, mapping changes, incident response, and eventual deprecation. Also document how a customer is notified of a breaking change and how an adapter is upgraded or retired.
Keep the shared route focused and reusable, make common variations composable, and keep exceptional behavior at an explicit boundary. That gives teams a concrete way to accept legitimate customer needs without letting every request redefine the product’s integration contract.
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