CluedIn for Solution Architects

CluedIn for Solution Architects — Integration & Reference Architecture Guide

Audience: Solution/Enterprise Architects, Platform Owners, Domain Architects
Goal: Explain where CluedIn fits in the wider data landscape, which architectural patterns it enables, and how to integrate it cleanly with surrounding systems (sources, lakes/warehouses, catalogs, BI/AI/Apps, and governance).

Assumptions: You favor event-driven, ELT, infrastructure-as-code, and least-privilege security. This guide is platform-agnostic with callouts for Azure/AWS/GCP and the Microsoft Power Platform.

1) What CluedIn Is (and Isn’t)

  • Is: a data unification + quality platform that ingests raw data, maps to canonical entities, enables cleaning, validation, dedup/golden records, and exports reliable outputs to your downstream systems. It supports streaming and batch, policy-based access, and auditability, with AI Agents for analysis/suggestions.
  • Is not: a heavy ETL monolith, BI semantic layer, or a general-purpose orchestrator. It plays with those systems, not instead of them.

Positioning vs common components

  • With ETL/ELT tools (ADF/Airflow/Glue/Fabric): CluedIn owns entity modeling, quality, unification; your orchestrator triggers/schedules.
  • With Lake/Warehouse (ADLS/S3/GCS + Fabric/Synapse/BigQuery/Snowflake): CluedIn exports golden/wide/star data to these stores.
  • With MDM: CluedIn’s dedup + survivorship can act as operational MDM-lite or feed/consume a dedicated MDM if present.
  • With Catalog/Lineage (Purview/Data Catalog/Collibra): CluedIn publishes metadata/lineage and respects classifications.
  • With Reverse ETL / Apps (Power Apps/Automate, Kafka topics, APIs): CluedIn provides operationalized, trustworthy datasets and events.

2) Reference Architectures

2.1 High-Level (Hybrid Streaming + Batch) 

[SaaS/DB/Events] ──► (Producers: CDC/HTTP/Kafka/S3) ──► CluedIn Ingestion Endpoints
       |                                                    │
       |                                              Raw Landing (ELT)
       |                                                    │
       └─────────────► (Optional Lake Zone) ◄───────────────┘
                                                           │
                                         Mapping ⇢ Cleaning ⇢ Validations ⇢ Dedup/Golden
                                                           │
                                      ┌─────────────────────┴─────────────────────┐
                                      │                                           │
                           Export Targets (Tables/Files)               Export Targets (Topics/APIs)
                         ► Warehouse/Lake (Snowflake/Fabric/        ► Kafka/Event Hub/Webhooks/Reverse‑ETL
                           BigQuery/Synapse/Databricks)             ► Apps/Operational CX
                                      │                                           │
                                  BI/AI/Analytics                         Apps/Workflows/Automation
                                      │                                           │
                         Catalog/Lineage (Purview/Collibra)        Observability (SIEM, APM, Dashboards)

2.2 Microsoft-Centric Blueprint 

Dataverse/D365/Apps → Power Automate (Flows) → CluedIn Ingestion (HTTP/Webhook/Event Hub)
                                            ↑                                       │
                                     Teams/Approvals                        AI Agents (masked)
ADLS/Fabric OneLake ← Exports (Parquet/SQL) ← CluedIn                         │
    Power BI / Fabric Semantic Model   ↑                                      │
Purview Catalog & Lineage ◄────────────┴────────────── Metadata/Lineage Push ◄┘

2.3 Data Mesh Placement

  • CluedIn can be a domain data product factory: each domain owns ingestion→golden→export with governance guardrails and shared platform controls (SSO, policies, audit, catalog).

3) Integration Patterns

3.1 Ingestion Into CluedIn

  • Streaming: Kafka/Event Hubs; prefer JSON lines, include event_id, updated_at, schema_version headers; key by business ID for ordering.
  • HTTP/Webhooks: simple producers (Power Automate/Apps, custom services) POST to ingestion endpoints; include idempotency keys.
  • Batch/Landing: S3/ADLS/GCS watchers for CSV/JSON/Parquet on a schedule; use manifests for completeness; automate backfills.
  • CDC: Debezium/ADF/Fabric Data Pipelines to stream DB changes; normalize op codes.

Architectural note: Keep producers dumb (no pre-clean). CluedIn is ELT: land first, transform inside via cleaning projects and policies.

3.2 Outbound from CluedIn

  • Tables/Views: export to warehouses (Fabric/Synapse, Snowflake, BigQuery, Databricks). Choose upsert with stable PKs; version breaking changes (_vN).
  • Files: Parquet to lake zones for batch analytics; partition by date/entity.
  • Events: publish to Kafka/Event Hubs when exports finish or when golden records change (perfect for Reverse ETL patterns).
  • APIs/Webhooks: push updates to SaaS; sign webhooks (HMAC) and design idempotent handlers.

3.3 Catalog & Lineage

  • Scan exported datasets from lake/warehouse with your catalog (Purview/Collibra/Glue).
  • Push process lineage (source → CluedIn → export) via Atlas/REST where supported.
  • Synchronize classifications/labels (PII/Restricted) and use them to drive policies in CluedIn and your BI/lake security layers.

3.4 Power Platform Integration

  • Power Automate: trigger on business events → call CluedIn ingestion endpoints; or receive CluedIn webhooks (DQ alerts, export complete) → route to Teams/Dataverse.
  • Power Apps: stewarding UIs (review duplicates, fix invalids) over CluedIn APIs.
  • Power BI/Fabric: consume versioned exports (star/wide); manage semantic models separately but aligned with CluedIn contracts.

4) Solution Design Decisions (Playbook)

4.1 Choosing Export Shapes

  • Star schema → BI at scale, facts + SCD2 dims.
  • Wide operational tables → CX apps, CRM backfills, support tooling.
  • Event streams → near‑real‑time syncs and automation.
  • API endpoints → per‑entity CRUD patterns are not the goal; favor batch/event outputs for scalability.

4.2 Identity & Golden Records

  • Start with deterministic dedup (email/phone/customer_id).
  • Define survivorship: source precedence + recency.
  • Emit stable entity IDs to downstreams to avoid churn.

4.3 Temporal Strategy

  • Decide SCD1 vs SCD2 per entity; BI usually needs SCD2 for dims.
  • For events, carry event_time (business) and ingest_time (processing).

4.4 Contracts & Versioning

  • Every export has a contract (schema, keys, SLA, labels).
  • Breaking changes → new _vN and a dual‑run window with migration notes.

4.5 Governance Hooks

  • Labels (PII, Restricted) at field/entity drive masking and approval workflows (e.g., PII export requires Governance approval).
  • Audit logs must cover promotions, policy changes, merges, and deletions.

5) End‑to‑End Blueprints

5.1 Real‑Time Customer 360 (Ops + BI)

  1. Sources: CRM, Support, Marketing streams.
  2. Ingest: Kafka topics into CluedIn; CDC for legacy DB.
  3. Unify: Mapping + cleaning + deterministic dedup → Person golden.
  4. Exports:
    • customers_wide_v1 (ops wide) → CRM/Support via Reverse ETL.
    • customers_dim_v1 + orders_fct_v1 → warehouse for BI.
  5. Catalog: Purview scan + lineage push.
  6. Apps/AI: Power Apps for stewarding; AI Agent reports DQ trends weekly.

5.2 B2B Product Catalog Harmonization

  • Ingest vendor feeds (S3/HTTP) + ERP/Kanban events.
  • Standardize attributes (units, categories), deduplicate SKUs.
  • Exports to commerce PIM and search index; event stream on SKU changes.
  • DQ KPIs: completeness of attributes, invalid GTIN rate, duplicate rate.

5.3 “Data Quality Firewall” for Downstream

  • Ingest raw; validate with rules (regex, referential, domain lists).
  • Quarantine failures; webhook to ticketing; weekly auto‑fixes via cleaning projects.
  • Export only passing records to a downstream operational DB.
  • Metrics: validity %, quarantine count, MTTR per issue type.

6) Security, Privacy & Compliance (Architect View)

  • SSO‑only with OIDC/SAML; SCIM for lifecycle; group‑to‑role mapping.
  • Least privilege roles; feature toggles isolated per env.
  • Data policies: column masking, row filters, deny‑by‑default for sensitive exports.
  • PII handling: masked views for AI Agents and non‑prod environments.
  • Secrets in a vault; short‑lived tokens; IP allowlists on webhooks.
  • Retention & legal holds: encode as policies; export destruction evidence.
  • Multi‑region: active/active for ingestion; exports regionally localized; clear RPO/RTO.

7) Non‑Functional Requirements (NFRs)

NFR Guidance
SLA (freshness) Export contracts with freshness_p95_minutes; alarms on breach.
Latency For near‑real‑time, prefer event pipelines with small batches.
Throughput Partition events; scale consumers; avoid tiny files.
Resilience DLQ + replay; idempotent producers; backpressure control.
Observability Correlation IDs; structured logs; dashboards for volumes/latency/errors.
Cost Columnar formats; partition strategy; right‑size schedules.
Compliance Immutable audit logs; approvals for PII exports; evidence packs.

8) Orchestration & CI/CD

  • Use Airflow/ADF/Fabric Data Pipelines/GitHub Actions to deploy configs (mappings, cleaning, policies, exports) as code.
  • Promotion: PR → staging diff → prod, with a change window and rollback plan.
  • Tests: schema/domain/FK/contract checks; regression diffs across exports.
  • Automate lineage publishing post‑export and catalog scans.

9) Observability & Runbooks

  • Dashboards: ingestion success/latency, export success/rows, DQ KPIs, dedup queue size.
  • Alerts: export failure, schema drift, DQ breach (validity/duplicate spikes), webhook retry storms.
  • Runbooks: ingestion 4xx/5xx spikes, export schema drift, duplicate surges, SLA breaches; each with contain/diagnose/rollback/backfill steps.

10) Decision Helpers (When to…)

  • Use CluedIn mapping vs ETL transforms?
    • Entity semantics and unification → CluedIn.
    • Heavy joins/enrichment external to CluedIn or compute‑intensive transforms → ETL/warehouse.
  • Star vs Wide exports?
    • BI → Star (SCD2 dims + facts).
    • Operational syncs/apps → Wide.
  • Streaming vs Batch ingestion?
    • Event‑driven, low‑latency needs → Streaming.
    • Bulk daily snapshots/backfills → Batch.
  • Deterministic vs Fuzzy dedup?
    • Start deterministic; add fuzzy rules off‑peak once precision is measured.

11) Templates (Copy/Paste)

11.1 Export Contract (with governance fields) 

name: customers_wide_v1
owner: "Sales Ops"
primary_key: customer_id
delivery: { type: sql-table, schedule: hourly }
sla: { freshness_p95_minutes: 60 }
labels: ["PII:email"]
compatibility: additive_only
lineage_required: true
approval_required_when_labels: ["PII","Restricted"]

11.2 Policy Sketches 

policy: mask_email_default
target: entity:Person.field:email
actions: [read]
effect: allow_with_mask
mask: "partial_email"
unless: [{ role_in: ["Data Steward","Administrator"] }]

policy: export_requires_approval
target: export:*
actions: [promote]
effect: require_approval
when: "export.contains_label('PII')"
approvers: ["Governance Manager","DPO"]

11.3 Webhook Registration (pseudo) 

POST /api/webhooks
{
  "name": "teams-export-success",
  "events": ["export.succeeded"],
  "url": "https://example.com/hooks/teams",
  "secret": "<HMAC>"
}

12) Operating Cadence (Architecture Guild)

Bi‑weekly design review: incoming sources, export changes, lineage completeness, PII exposure checks.
Monthly scorecard: export contract coverage, DQ trend, incident classes, cost, catalog lineage %, AI usage review.
Quarterly roadmap: domains onboarding, deprecations of _v0, platform upgrades, governance maturity.

13) Anti‑Patterns to Avoid

  • Designing a grand unified model before shipping any export.
  • Pre‑cleaning upstream; defeating ELT and lineage transparency.
  • Shipping breaking changes without contracts/versioning and a dual‑run window.
  • Treating AI Agents as unrestricted data readers; avoid unmasked access.
  • Ignoring catalog/lineage; producing orphaned BI datasets.

14) One‑Page Checklist (Print This)

  • Ingestion endpoints (stream/batch) automated, no manual uploads.
  • Minimal mapping → exports live → iterate mapping/cleaning.
  • Dedup deterministic first; survivorship clarified.
  • Contracts for each export; _vN for breakers.
  • Policies/labels on PII; approvals enforced.
  • Catalog & lineage integrated; Purview scan/push verified.
  • Dashboards + alerts + runbooks in place.
  • CI/CD promotion path; rollback tested; audit logs retained.

Bottom line for architects: Place CluedIn at the heart of unification and quality, bridging raw sources and trustworthy consumer outputs. Keep it event‑friendly, contract‑driven, catalog‑connected, and governed by policy. Integrate tightly with your orchestrator, lake/warehouse, catalog, Power Platform/Apps, and SIEM—so the rest of your architecture can move faster with confidence.