Topic
Embedded analytics architecture
The architecture decisions in embedded analytics are mostly about two things: keeping one customer's rows away from another's, and deciding where the metric definitions live. Everything else follows from those two.
Start hereWhat Is Headless BI? One Metrics Layer for Any Front EndHeadless BI separates the metrics layer from whatever draws the charts. The three layers, where SDK and API differ, and the build-versus-buy economics that follow.
Vocabulary
Definitions.
- TermMulti-TenancyMulti-tenancy is a software architecture in which one solution serves multiple customer or organizational tenants while applying explicit isolation, routing, resource, and operating controls.
- TermRow-Level SecurityRow-level security (RLS) restricts which rows an execution context may read or modify, but it is one control within a complete authorization design.
- TermJWT AuthenticationA JWT proves where a claim set came from, not that the claims are true. In an embedded dashboard the harder question is where the row scope sits, whether it travels inside the signature, and what happens when it disagrees with the row policy.
- TermiFrame EmbeddingiFrame embedding allows you to display external content within your web page. Learn how iFrames work in embedded analytics and when to use them.
- TermSDK IntegrationSDK integration is the process of adding a software development kit to your application, enabling faster development and smooth connectivity with platform APIs through pre-built tools and libraries.
- TermHeadless BIHeadless BI separates analytical presentation from semantic and query services so multiple interfaces can consume a governed analytical contract.
- TermReal-Time AnalyticsReal-time analytics processes and analyzes data instantly as it's generated, enabling immediate insights and faster decision-making for B2B SaaS products.
Tenant isolation
Multi-tenant architecture and row-level security.
Metrics layer
Headless BI and the semantic layer.
- ArticleWhat Is Headless BI Architecture? Benefits, Trade-offs and When to Use ItWhy engineering teams are choosing headless architectures for embedded analytics, and when traditional approaches still make sense.
- ArticleWhat Is API-First Analytics? Workflow, Code and ChecklistTraditional BI tools force you to build through their UI. API-first analytics flips this, giving developers programmatic control over embedded experiences.
- ArticleSDK-First Analytics: Component API vs Iframe ManagementMost teams pick between iframe and SDK based on speed. The real question is: how much control does your product roadmap actually need?
Real time
Streaming architecture and freshness.
- ArticleReal-Time Data Visualization: When It Actually MattersMost SaaS products don't need true real-time analytics. Here's how to know when you do, and what it takes to deliver it.
- ArticleLive Dashboard Best Practices for Real-Time DataChoose live-dashboard refresh, caching, alerting, and audience views from decision latency and measurable system constraints.
- ArticleStreaming Dashboard Architecture: Design for Decision-Safe StateChoose streaming from decision latency, then define event time, watermarks, late data, corrections, backpressure, serving state, and recovery.
By the audience it reports to
The same topic, by industry.
- Sales and CRMPipeline and CRM reporting inside your product
- Operations and Supply ChainOperations and supply chain analytics, with per-number freshness
- FinanceFinance analytics, reconciled to the ledger
- Retail and EcommerceRetail and ecommerce analytics, with comparable store windows
- HealthcareHealthcare analytics, with source reconciliation and access by care relationship
- MarketingMarketing analytics for agencies, white-label per client
- HRHR analytics, scoped by reporting line
Also in this topic
- GuideSemantic Layer for Embedded Analytics: One Definition Across Many TenantsRun a semantic layer in a multi-tenant analytics product with one shared definition per metric, customer changes as declared extensions, and versioned definition changes that protect existing dashboards.
- TermContext LayerA context layer gives an AI agent the business definitions, relationships, examples and rules it needs to answer a data question correctly, on top of the data itself.
- TermSemantic LayerA semantic layer maps raw tables to named business metrics and dimensions, defined once, so every dashboard, embedded view and AI assistant computes the same number the same way.