CluedIn for System Integrators

CluedIn for System Integrators — Integration Cookbook

Audience: System Integrators, Professional Services, SI practice leads
Goal: Provide proven patterns, templates, and guardrails for integrating CluedIn with the wider ecosystem: Data Lakes, Data Engineering tools, Data Governance tools, Power Platform & AI stacks (Power Automate, Power Apps, Copilot, Azure AI Foundry, OpenAI, Claude, Ollama), and Dataverse.

Principles: ELT-first, event-friendly, contracts & policies as code, least privilege, and observability by default.

0) Engagement Blueprint (First Week)

  • Day 1–2: Confirm SSO/roles, network allowlists, non-prod workspace, export destinations.
  • Day 3–4: Stand up one Ingestion Endpoint (stream or batch) + one Export Target (table or file).
  • Day 5: Wire catalog scan/lineage, alerts, and a demo to Power BI/Apps.
  • Deliverables: Diagrams (L0/L1), export contract, policy/masking file, runbook, and CI pipeline.

1) Patterns Overview

Integration Area Primary Pattern Protocols Artifacts to Deliver
Data Lakes Export Parquet/Delta, partitioned S3/ADLS/GCS Export config, partition spec, retention policy
Data Engineering Orchestrate deploy/run/monitor Airflow/ADF/Fabric/Glue DAG/pipeline JSON/YAML, webhook triggers, CI
Data Governance Catalog scan + lineage push Purview/Collibra/Atlas Scan config, lineage job, label mapping
Power Platform & AI Event in/out, API calls, guarded prompts HTTP/Kafka/Webhooks/OAuth Flow/App sketches, HMAC verify, AI guardrails
Dataverse Delta → CluedIn, exports back to tables Dataverse APIs, Power Automate Connector flows, backfill scripts, SLA docs

2) Data Lakes (ADLS / S3 / GCS)

2.1 Export Shapes

  • Columnar files: Parquet (preferred), optional Delta/iceberg in your lake.
  • Partitioning: by load_date or business date (e.g., event_date=YYYY-MM-DD).
  • Layout: s3://bucket/mdm/customers_wide_v1/event_date=2025-08-24/part-000.parquet

2.2 Export Config (pseudo-JSON) 

{
  "name": "customers_wide_v1",
  "type": "file-parquet",
  "options": {
    "connection": "s3://company-analytics",
    "path": "mdm/customers_wide_v1/",
    "partition_by": ["event_date"],
    "compression": "snappy",
    "overwrite": false
  },
  "schedule": "0 * * * *"
}

2.3 Lake Guardrails

  • Small files kill performance: use 128–512MB target file sizes.
  • Schema evolution: additive by default; break → new _vN path.
  • Retention: lifecycle policies in S3/ADLS; document RPO/RTO.

3) Data Engineering Tools

3.1 Airflow DAG (sketch) 

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
import requests, os

def trigger_export(**ctx):
    r = requests.post(
        f"{os.environ['CLUE_HOST']}/api/exports/run",
        json={"name":"customers_wide_v1"},
        headers={
            "Authorization": f"Bearer {os.environ['CLUE_TOKEN']}",
            "X-Correlation-Id": ctx['run_id']
        }, timeout=30)
    r.raise_for_status()

with DAG("cluedin_customers_export", start_date=datetime(2025,8,1), schedule="@hourly", catchup=False) as dag:
    run = PythonOperator(task_id="run_export", python_callable=trigger_export)

3.2 Azure Data Factory / Fabric Data Pipelines

  • Use Web activity to POST to CluedIn (ingest/export).
  • Handle retry with exponential backoff; bubble up non-2xx.
  • Emit correlation_id from pipeline run ID.

ADF Web activity body (example)

{ "name": "warehouse-contacts-v1" }

3.3 AWS Glue / GCP

  • Use Job step to call CluedIn APIs or read CluedIn exports.
  • Favor Parquet reads; push metrics back to CloudWatch/Stackdriver.

3.4 dbt / Semantic Layers

  • dbt models consume CluedIn exports; keep contracts aligned.
  • Avoid heavy transformations in CluedIn mapping when dbt is authoritative downstream.

4) Data Governance Tools

4.1 Purview / Collibra Catalog Scans

  • Register warehouse/lake connections where CluedIn writes.
  • Schedule hourly/daily scans; tag datasets with owners and classifications.

4.2 Lineage (Atlas-style) 

POST https://<purview>/api/atlas/v2/lineage
{
  "process": { "typeName": "cluedin_export", "attributes": {
    "name":"customers_wide_v1","qualifiedName":"cluedin.export.customers_wide_v1"} },
  "inputs": [{"qualifiedName":"cluedin.entity.Person"}],
  "outputs": [{"qualifiedName":"s3://company-analytics/mdm/customers_wide_v1"}]
}

4.3 Classification Sync

  • Map CluedIn labels (PII, Restricted) to catalog classifications.
  • Drive masking policies consistently in BI and lake security layers.

5) Power Platform & AI Stacks

5.1 Power Automate (Flow) → CluedIn Ingestion

  • Trigger: When a row is added in Dataverse (or any connector).
  • Action: HTTP POST to CluedIn /api/ingest with OAuth token.

Flow HTTP action body

{
  "source": "dataverse-contacts",
  "payload": {
    "id": "@{triggerBody()?['contactid']}",
    "email": "@{triggerBody()?['emailaddress1']}",
    "updated_at": "@{utcNow()}"
  }
}

5.2 CluedIn Webhook → Flow / Teams

  • Register export.succeeded webhook → verify HMAC → post to Teams.

Node verify sketch

const crypto = require("crypto");
function verify(sig, raw, secret){
  const h = crypto.createHmac("sha256", secret).update(raw).digest("hex");
  return crypto.timingSafeEqual(Buffer.from(sig,"hex"), Buffer.from(h,"hex"));
}

5.3 Power Apps

  • Stewarding app over CluedIn APIs (dedup review, fix invalids).
  • Use service principal; enforce role checks server-side.

5.4 Copilot (Power Platform)

  • Feed versioned exports with clear semantics.
  • Mask PII for prompts; restrict to masked views for exploratory agents.

5.5 Azure AI Foundry / OpenAI / Claude / Ollama

Pattern: AI Agents with Guardrails

  • Read scope: masked datasets or sample subsets.
  • Modes: analysis and suggest; no auto_fix until reviewed.
  • Prompt logging: store prompt/response IDs with correlation_id.
  • PII/Secrets: redact before sending to APIs; keep model configs in code.

Example: calling an AI analysis job (pseudo)

POST /api/ai/agents/run
{
  "agent": "dq-analyzer",
  "target": { "entity": "Person" },
  "mode": "analysis",
  "options": { "sample": 5000 }
}

Local inference (Ollama)

  • Use Ollama for in‑VPC/in‑laptop development; treat it like any external AI: masked data only and clear retention policy.

6) Dataverse Integration

6.1 Inbound (to CluedIn)

  • Dataverse → Flow → CluedIn via HTTP ingestion for deltas.
  • For backfills, use Dataverse Web API paging and push batches to CluedIn.
  • Emit idempotency key (id + updated_at) and correlation_id.

6.2 Outbound (to Dataverse)

  • Consume CluedIn export (wide table) and Upsert to Dataverse entity via Web API.
  • Respect concurrency (If‑Match ETag) and retry on 429s.

Dataverse upsert (HTTP)

PATCH https://<org>.crm.dynamics.com/api/data/v9.2/contacts(<guid>)
If-Match: *
Content-Type: application/json

{ "emailaddress1": "a@example.com", "firstname": "Ada", "lastname": "Lovelace" }

6.3 Identity & Ownership

  • Align Person/Account keys with Dataverse GUIDs or keep a link table in the export.
  • Document survivorship rules in the runbook for conflicts.

7) Security & Compliance Playbook

  • SSO-only with OIDC/SAML; group‑based roles mapped to least privilege.
  • Tokens: short-lived; rotate ≤ 90 days; store in a vault; audit usage.
  • Policies: column masking (PII), row filters (region/tenant), export promotion approvals for sensitive labels.
  • Webhooks: HMAC signatures; allowlist source IPs; idempotent handlers.
  • AI: masked datasets by default; prompt logging; model registry with owner/purpose.
  • Audit: retain logs for 1–7 years; include SSO, role changes, tokens, policy updates, merges, and export promotions.

8) Observability & SLAs

  • Metrics: ingestion success, DLQ size, export rows/latency, DQ KPIs (validity, completeness, duplicates), webhook retries.
  • Dashboards: per domain/export; include top error classes and freshness.
  • Alerts: export failure, schema drift, DQ breach, 429 storms, unusual token usage.
  • Runbooks: incident steps—contain, diagnose, rollback, backfill—with correlation_id examples.

9) CI/CD & Config-as-Code

  • Keep mappings, cleaning, validations, policies, and exports in Git.
  • Pipeline: PR → staging export diff → approval → prod deploy.
  • Validate with schema/domain/FK tests and export contract checks; publish release notes.

GitHub Actions sketch

name: cluedin-deploy
on: [pull_request, push]
jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: ./tools/validate-config.sh
  deploy-staging:
    if: github.ref == 'refs/heads/main'
    steps:
      - run: ./tools/apply.sh env/test
  deploy-prod:
    needs: deploy-staging
    steps:
      - run: ./tools/apply.sh env/prod

10) Ready-to-Use Templates

10.1 Export Contract (with governance) 

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

10.2 Policy: Mask Email for Non-Owners 

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

10.3 Webhook Registration 

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

10.4 Airflow Backfill Operator (sketch) 

def backfill(start, end, step="1d"):
    # call CluedIn backfill endpoint or replay DLQ in windows
    ...

11) Common Pitfalls & How to Avoid Them

  • Pre-cleaning upstream → breaks lineage and duplicates work. Use CluedIn cleaning projects.
  • No contracts/versioning → downstream breakage. Always publish contracts and bump _vN on breakers.
  • Tiny Parquet files → slow analytics. Batch/compact.
  • Unbounded AI access → privacy risk. Enforce masked views + logs.
  • Webhook handlers not idempotent → duplicate side effects. Store event IDs and use upserts.

12) One-Page SI Checklist

  • Ingestion endpoints automated; no manual uploads.
  • One end‑to‑end in→map→clean→dedup→out path live.
  • Export contract + partitioning + retention documented.
  • Catalog scan + lineage push configured.
  • Policies/labels wired; approvals enforced for PII exports.
  • Dashboards + alerts + runbooks active.
  • CI/CD promotion path with rollback tested.
  • Power Platform/AI demos working end‑to‑end.
  • Dataverse upsert path proven with concurrency + rate limits.

Outcome: With these patterns and templates, you can plug CluedIn into lakes, warehouses, orchestration, catalogs, Power Platform & AI stacks, and Dataverse quickly—while preserving security, lineage, and SLAs. Clone the snippets, fill in environment details, and ship a thin slice first; iterate safely with contracts and policies.