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Platform Integration Architect

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Platform Integration Architect Study Checklist
  • Platform Administrator
  • Platform App Builder
  • Platform Foundations
  • Platform Developer
  • Platform Administrator II
  • Agentforce Sales Consultant
  • Agentforce Service Consultant
  • Platform Data Architect
  • Platform Development Lifecycle and Deployment Architect
  • Platform Identity and Access Management Architect
  • Platform Integration Architect
  • Platform Sharing and Visibility Architect
  • Heroku Architect
  • B2C Solution Architect
  • Experience Cloud Consultant
  • Agentforce Field Service and Operations Consultant
  • Agentforce Nonprofit Consultant
  • Data 360 Consultant
  • Omnistudio Consultant
  • CRM Analytics and Einstein Discovery Consultant
  • Platform User Experience Designer
  • Platform Strategy Designer
  • B2C Commerce Developer
  • JavaScript Developer
  • Omnistudio Developer
  • Platform Developer II
  • Marketing Cloud Engagement Administrator
  • Marketing Cloud Engagement Specialist
  • Marketing Cloud Engagement Consultant
  • Agentforce Sales Foundations
  • Business Analyst
  • Marketing Cloud Engagement Developer
  • Marketing Cloud Engagement Foundations
  • Agentforce Specialist
  • Agentforce Life Sciences Consultant
  • B2B Commerce Administrator AP
  • B2B Commerce Developer AP
  • Agentforce Consumer Goods AP
  • Agentforce Financial Services AP
  • Agentforce Health AP
  • Agentforce Manufacturing AP
  • MuleSoft Integration Foundations
  • MuleSoft Developer
  • MuleSoft Developer II
  • MuleSoft Platform Integration Architect
  • MuleSoft Platform Architect
  • Tableau Desktop Foundations
  • Tableau Data Analyst
  • Tableau Consultant
  • Tableau Server Administrator
  • Tableau Architect
  • Slack Administrator

Checklist progressProgress

0/179Learned

  • Inventorying systems, integration points and standards in the current landscape
  • Identifying the system of record and replication needs across a landscape
  • External system protocols versus Salesforce integration mechanisms
  • Network and transport boundaries for on-premise and legacy endpoints
  • Platform limits and allocations as landscape boundaries
  • Reusing existing ESB or ETL middleware versus point-to-point integrations
  • Swivel-chair work and stale data copies as landscape pain points
  • Conflicting sources of truth and duplicate records across systems
  • Daily API allocation pressure from existing integrations
  • Capacity and availability limits of legacy back-end systems
  • Transformation complexity in the integration layer
  • Isolating the decisive constraint in an existing integration landscape
  • External client apps versus connected apps for integration access
  • Server-to-server OAuth flow choice for inbound integrations
  • Legacy username-password and user-agent flows in an existing landscape
  • Login and session policies that protect integration accounts
  • Integrating Salesforce with an existing enterprise identity provider
  • Functional versus non-functional integration requirements
  • Integration type and architecture layer of a requirement
  • Transformation, orchestration and aggregation needs in an integration
  • Data freshness and SLA requirements as integration timing drivers
  • Response-wait requirements for the calling system or user
  • Data volume and message size as integration requirements
  • Error handling and recovery expectations as integration requirements
  • Idempotency and duplicate-message handling requirements
  • Maintainability and operational support requirements for integrations
  • Classifying integration data into Confidential, Secure and Public tiers
  • Field-level versus object-level classification of integration payloads
  • Transport protection for integrations carrying highly sensitive data
  • Carrying data classification across integration boundaries
  • Single customer view as a driver of integration scope
  • System of record and data quality in CRM integrations
  • Automating manual cross-system business processes
  • CRM success metrics and their reporting data needs
  • Historical data migration versus ongoing synchronization
  • Projected growth against API and event allocations
  • Org design factors that limit integration throughput at scale
  • Point-to-point versus middleware as the integration landscape grows
  • Acquisitions and multi-org growth in integration choice
  • Replication versus virtualization as data volumes and retention grow
  • Privacy and data residency regulations versus replicating personal data
  • Payment card data and PCI DSS scope in integration design
  • Audit and traceability obligations for integration activity

Given an existing system landscape diagram, create an inventory of the systems and integration patterns

0/8

  • Data, process and virtual integration categories
  • UI, business process and data layers in a landscape inventory
  • Systems of record and source of truth in the inventory
  • Interface attributes to capture for each integration point
  • Integration direction and initiating system per interface
  • Point-to-point versus middleware-mediated connections
  • Classifying existing interfaces by Salesforce integration pattern
  • Existing integration mechanisms and the patterns they implement

Given a use case and business process, evaluate system and process constraints

0/7

  • System capabilities, failure handling and transactionality as process constraints
  • Tight versus loose coupling between process participants
  • Downtime and maintenance windows of process participants
  • Inbound API request allocations and concurrent request limits
  • Reaching on-premise systems behind a corporate firewall
  • Legacy protocols, file-based exchange and transformation constraints
  • Orchestrating multi-step processes across several systems

Given a use case, identify integration security / authentication / authorization requirements

0/7

  • Shared integration identity versus per-user identity
  • Dedicated API-only integration users and least privilege
  • OAuth flows for user-facing web, mobile and device clients
  • Client app policies and scopes for inbound API access
  • Federated identity and a central IdP for API access
  • Mutual TLS and certificate requirements for integrations
  • Encryption requirements for sensitive integration data

Given a use case, identify performance needs (volumes, response times, latency) and propose appropriate integration solutions that will meet business requirements

0/11

Sizing volume, throughput and latency needs into an integration SLA

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Synchronous versus asynchronous processing for response-time requirements

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Near-real-time versus batch synchronization for data freshness needs

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Data virtualization versus replication of external data

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REST or SOAP API versus Bulk API 2.0 for inbound record volumes

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Bulk load throughput planning and batch processing timeouts

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Large data volume extract performance through the APIs

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Reducing API round trips with Composite resources and GraphQL

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Event-driven patterns for volume surges and multi-subscriber fan-out

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Platform event and Change Data Capture delivery allocations

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Middleware queuing and throttling to absorb volume spikes

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  • Pattern timing, volume and direction as selection criteria
  • When to choose the Request and Reply pattern
  • When to choose the Fire and Forget pattern
  • When to choose the Remote Call-In pattern
  • UI Update Based on Data Changes pattern
  • When to choose Batch Data Synchronization
  • High-Frequency Data Replication versus batch synchronization
  • When to choose the Data Virtualization pattern
  • Publish/Subscribe pattern for notifying multiple subscribers
  • Event-driven architecture fit versus request-reply or batch patterns
  • Choosing among event-driven architecture patterns
  • Layer approach for mapping integration components
  • Middleware hub versus point-to-point integration
  • Components for a user-initiated synchronous callout
  • Asynchronous outbound components for fire-and-forget processing
  • Change Data Capture versus custom platform events for publishing changes
  • Salesforce Connect adapter selection for virtualized external data
  • Data integration tool selection by direction, licensing and landscape
  • UI-layer integration: Canvas, Lightning Out and mashups
  • Org-to-org integration options
  • Orchestration versus choreography and where orchestration lives
  • Coupling, availability and exception-handling trade-offs
  • Synchronous callout timeouts and long-running request limits
  • Trigger-initiated callouts and asynchronous Apex trade-offs
  • Outbound messaging delivery, endpoint security and limitations
  • Event bus allocations, add-ons and retention windows
  • Change Data Capture entity limits and gap or overflow events
  • Data virtualization versus data replication trade-offs
  • Large data volume constraints in batch synchronization
  • API request allocations versus hard governor limits
  • Transactional integrity and compensation across integrated systems
  • Protocols, data formats and communication modes of Salesforce APIs
  • API planning framework for choosing Salesforce APIs
  • REST API versus SOAP API for inbound record operations
  • Bulk API 2.0 versus composite and synchronous APIs by data volume
  • Bulk API 2.0 versus the original Bulk API
  • When to use GraphQL API for client-driven data retrieval
  • User Interface, Connect REST and Analytics REST APIs
  • Custom Apex REST or SOAP web services versus standard data APIs
  • Pub/Sub API versus Streaming API for external event subscribers
  • Metadata API versus Tooling API for metadata integrations
  • Named Credentials and External Credentials for outbound callouts
  • Network connectivity security for on-premises and private cloud systems
  • Data classification driving protection of integration data
  • Shield Platform Encryption choices for integrated data
  • State management and cross-system record keys
  • Salesforce as API provider versus API consumer
  • Enterprise WSDL, Partner WSDL and REST API for remote callers
  • Inbound event notification through platform event publishing
  • Request and Reply versus Fire and Forget for outbound calls
  • SOAP web service callouts versus HTTP callouts
  • External Services and Flow HTTP Callout for low-code outbound calls
  • Asynchronous Apex callouts versus platform events at scale
  • Middleware versus point-to-point callouts from Salesforce
  • Outbound data sync tool choice by timing, direction and licensing
  • Daily API request allocation across multiple integrations
  • When asynchronous processing suits a scalable integration
  • Long-running synchronous callouts and concurrent request limits
  • Automation and sharing overhead on high-volume inbound loads
  • Record locking, data skew and integration governance
  • Peak-load headroom and realistic-volume scale testing
  • Error handling and recovery in Request and Reply callouts
  • Error handling and recovery in Fire and Forget integrations
  • Error handling responsibility in Remote Call-In integrations
  • Partial success versus all-or-none behavior for inbound API calls
  • Error handling and recovery in Batch Data Synchronization
  • Retry, notify and fail requirements in integration tool choice
  • OAuth flow selection for inbound system-to-system integrations
  • Compensating login controls for API-only integration accounts
  • Data access enforcement for standard APIs versus custom Apex web services
  • Named principal versus per-user principal for authenticated callouts
  • Protecting classified data sent from Salesforce to external systems
  • Private Connect for Salesforce and AWS integration traffic
  • Loose coupling to absorb upgrades in connected systems
  • Delivery guarantees and data integrity while a target system is offline
  • Circuit breakers and graceful degradation for unavailable endpoints
  • Protecting integrations through Salesforce seasonal releases
  • Salesforce maintenance windows and instance moves
  • Integration SLIs and SLOs derived from business requirements
  • Monitoring and reporting API consumption against org allocations
  • Monitoring Bulk API jobs and asynchronous Apex processing
  • Integration latency percentiles and per-step transaction timing
  • Scale Center versus Proactive Monitoring for integration load health
  • Correlation IDs and contextual logging for cross-system tracing
  • Layered detection of integration failures before users report them
  • Error handling, transactionality and recovery by integration pattern
  • Recovery actions for different integration error categories
  • Exception capture and IT operations alerting for failed integrations
  • Flow fault paths versus default flow failure handling for callouts
  • Outbound message retry behavior and failed delivery queue
  • Middleware queuing and dead-letter handling for undeliverable messages
  • Event subscriber recovery with replay IDs and event retention
  • Change Data Capture gap and overflow event recovery
  • Bulk-proof backlog recovery after integration downtime
  • Escalation paths, runbooks and post-incident review for integration incidents
  • Custom integration log object versus record-level sync status fields
  • Platform event logging versus direct DML under transaction rollback
  • Event Monitoring event types for API and integration activity
  • Event log file delivery timing, retention and durability
  • Event Monitoring Analytics App versus external log aggregation
  • Audit reporting on data changes made by integration users
  • Business-flow success reporting versus component health metrics
  • Real-time alerting versus scheduled reports for integration monitoring

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  1. Platform Integration Architect Checklist
  3. Translate Needs to Integration Requirements

Event-driven patterns for volume surges and multi-subscriber fan-out

Explainer

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Practice Question

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Next conceptPlatform event and Change Data Capture delivery allocations

Checklist progress

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  1. Platform Integration Architect Checklist
  3. Translate Needs to Integration Requirements
Event-driven patterns for volume surges and multi-subscriber fan-out

Explainer

Learn More

Practice Question

Keep going

Next conceptPlatform event and Change Data Capture delivery allocations

Explainer

Event-driven patterns let a system announce a change once, as an event, so that any number of other systems can react to it in near real time. They replace fragile point-to-point links, and they help absorb surges when messages arrive faster than the receiving systems can handle them. They are for asynchronous processes, not for processes where a person waits for an answer.
Core information
  • Use an event-driven architecture for processes that do not need a synchronous response. Good fits include near real-time notifications to many applications, spreading heavy processing across several systems, absorbing message surges, IoT devices, and systems or mobile devices that are sometimes offline.
More details and nuances
  • Fanout trades visibility for speed. Because all subscribers share one message, it is harder to verify whether a particular subscriber received it. When you must know which subscriber got each message, Queuing (one queue per subscriber) or Streaming (one copy per subscriber) fits better.

Explainer

Event-driven patterns let a system announce a change once, as an event, so that any number of other systems can react to it in near real time. They replace fragile point-to-point links, and they help absorb surges when messages arrive faster than the receiving systems can handle them. They are for asynchronous processes, not for processes where a person waits for an answer.
Core information
  • Use an event-driven architecture for processes that do not need a synchronous response. Good fits include near real-time notifications to many applications, spreading heavy processing across several systems, absorbing message surges, IoT devices, and systems or mobile devices that are sometimes offline.
More details and nuances
  • Fanout trades visibility for speed. Because all subscribers share one message, it is harder to verify whether a particular subscriber received it. When you must know which subscriber got each message, Queuing (one queue per subscriber) or Streaming (one copy per subscriber) fits better.