CluedIn for Data Engineers

CluedIn for Data Engineers — Build & Operate Handbook

Audience: Data Engineers, Analytics Engineers, MLEs
Goal: Provide a practical, end‑to‑end playbook for building reliable, secure, and observable pipelines in CluedIn—from ingestion through mapping, cleaning, exports, CI/CD, and operations.

This handbook favors configuration-as-code, small incremental releases, strong observability, and close collaboration with Admins and Stewards.

0) Your First 48 Hours (Checklist)

Access & Environment

  • Sign in via SSO; verify Data Engineer role permissions.
  • Identify dev/test/prod workspaces and which one you own.
  • Configure the CLI/API credentials (short‑lived if possible).

Repos & Config-as-Code

  • Clone the platform-config repo (mappings, policies, projects).
  • Set up a branching model (feature → PR → staging → prod).
  • Install pre-commit linters for YAML/JSON schema validations.

Pipelines

  • Create one Ingestion Endpoint for your first source (stream or scheduled batch).
  • Wire one Export Target (table/topic/API) for an end‑to‑end path.
  • Add a staging export to test mapping/cleaning changes safely.

Observability

  • Pin dashboards for ingestion/export success, latency, rows.
  • Learn where to pull logs and audit logs with correlation_id.
  • Set baseline alerts (failures, volume anomalies, schema drift).

1) Role Scope (What You Own)

  • Data ingress (HTTP/Kafka/webhooks/batch/CDC) and reliability (retries, DLQ, replay).
  • Mapping of sources → CluedIn entities/relationships; schema evolution.
  • Cleaning projects and validations implementation with Stewards.
  • Exports to warehouses, lakes, topics, APIs; versioning and contracts.
  • Automation (schedules, webhooks) and integration (Power Platform, Purview).
  • Observability (logs, metrics, tracing), cost/perf, and incident response.
  • Security (secrets, tokens, PII handling) in partnership with Admins.

2) Environments & Config as Code

2.1 Workspace Layout

  • dev: fast iteration, broad logs, feature flags on.
  • test/staging: PR validation, near‑prod data scale, alerts to engineers.
  • prod: tight scopes, change windows, alerts to on‑call.

2.2 Config Repository

Keep mappings, policies, cleaning projects, and export configs in a Git repo:

/cluedin/
  mappings/
  cleaning/
  exports/
  policies/
  ai-agents/
  env/
    dev/
    test/
    prod/

Use overlay variables per env (URIs, secrets references, schedules).

2.3 Promotion

  • PR → staging export diff → approval → prod.
  • Always include: change summary, risk, rollback, owner, metrics to watch.

3) Ingestion: Endpoints, Streams, Batches

Prefer automated producers; avoid manual uploads after day 1.

3.1 HTTP/Webhook (JSON lines) 

curl -X POST \
  -H "Authorization: Bearer $CLUEDIN_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"source":"crm-contacts","payload":{"id":"c_123","email":"a@example.com","updated_at":"2025-08-22T12:00:00Z"}}' \
  https://<YOUR_INGESTION_ENDPOINT>/ingest

3.2 Kafka / Event Hub (stream)

  • Topic per domain or entity; include schema version in headers.
  • Enforce idempotent producers; include event_id and updated_at.

3.3 Batch (S3/Blob/ADLS) 

{
  "name": "s3-sales-orders",
  "type": "s3-bucket-watch",
  "options": {
    "bucket": "acme-prod-orders",
    "prefix": "daily/",
    "file_types": ["json","csv","parquet"],
    "schedule": "cron(0 * * * ? *)"
  }
}

3.4 CDC (Databases)

  • Use Debezium/Log-based CDC to stream inserts/updates/deletes.
  • Normalize op codes upstream; ensure primary keys present.

3.5 Reliability Patterns

  • Retries with exponential backoff; cap attempts.
  • DLQ/Quarantine for poison messages; add a replay tool.
  • Backfills: separate lane (lower priority), preserve current SLAs.

4) Mapping: From Raw to Entities

4.1 Principles

  • Start minimal: keys, names, timestamps, core relations.
  • Keep heavy standardization in cleaning projects, not mapping.
  • Make mapping versioned and revertible.

4.2 Example Mapping (pseudo-YAML) 

entity: Person
source: "crm-contacts"
fields:
  id: $.id
  email: $.email
  first_name: $.first_name
  last_name: $.last_name
  updated_at: $.updated_at
relationships:
  - type: "EMPLOYED_BY"
    to_entity: Organization
    from: $.org_id
    to: $.organization.id

4.3 Schema Evolution

  • Additive first (new nullable fields).
  • For breaking changes, create _v2 mapping and staging export to test.

5) Cleaning Projects & Validations

5.1 Cleaning (incremental, idempotent) 

project: normalize_contacts
schedule: "0 * * * *"  # hourly
steps:
  - name: normalize_email
    action: set
    field: email
    value: lower(trim(email))
  - name: e164_phone
    when: phone is not null
    action: set
    field: phone
    value: to_e164(phone, default_country="US")

5.2 Validations (guardrails) 

rule: email_regex
entity: Person
check:
  regex: "^[^@\\s]+@[^@\\s]+\\.[^@\\s]+$"
severity: high
on_fail: { action: "flag" }

Work with Stewards for rules and thresholds; engineers implement safely.

6) Exports: Tables, Topics, APIs, Files

6.1 Contract & Versioning

Define a data contract per export (schema, keys, SLA, semantics). Bump version on breaking changes (contacts_v1 → contacts_v2).

6.2 Example Export Config (pseudo-JSON) 

{
  "name": "warehouse-contacts-v1",
  "type": "sql-table",
  "options": {
    "connection": "analytics-warehouse",
    "schema": "mdm",
    "table": "contacts_v1",
    "mode": "upsert",
    "primary_key": ["contact_id"]
  },
  "mapping": {
    "contact_id": "Person.id",
    "email": "Person.email",
    "first_name": "Person.first_name",
    "last_name": "Person.last_name",
    "updated_at": "Person.updated_at"
  },
  "schedule": "0 * * * *"
}

6.3 Reliability

  • Upserts require stable primary keys.
  • Add row count and null ratio checks per run.
  • Emit correlation_id to trace in logs.

7) Orchestration & Triggers

  • Use CluedIn schedules for simple jobs.
  • For complex DAGs, trigger CluedIn via webhooks from Airflow/ADF.
  • Emit success/failure webhooks to Power Automate or incident channels.

8) Testing Strategy

8.1 Unit & Transform Tests

  • Functions: normalization, parsing, enrichment.
  • Golden datasets for edge cases (UTF‑8, nulls, long strings).

8.2 Contract Tests

  • Validate input fields/types/required keys.
  • Validate export schema and SLA (latency, freshness).

8.3 Regression Diffs

  • Compare staging vs prod exports for row counts, nulls, distribution drift.

8.4 CI Example (pseudo-YAML) 

name: cluedin-ci
on: [pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: pip install -r requirements.txt
      - run: pytest -q
      - run: ./tools/validate-config.sh  # schema check for YAML/JSON

9) Observability: Logs, Metrics, Tracing

9.1 What to Log

  • Ingestion: status, bytes, parse errors, DLQ counts.
  • Mapping/Cleaning: records touched, rule breaches.
  • Exports: run duration, rows out, schema diffs.

9.2 Metrics to Track

  • Freshness/latency per pipeline stage.
  • Completeness/validity/duplicates (with Stewards).
  • Failure rate by category.
  • Cost signals (records processed, reprocess volume).

9.3 Correlation

  • Pass a correlation_id end‑to‑end; include it in user‑visible errors.

10) Performance & Cost Tuning

  • Choose columnar formats for heavy batch (Parquet).
  • Partition by time/entity; avoid small files.
  • Tune batch size and parallelism; monitor backpressure.
  • Cache reference data; avoid N+1 lookups in hot loops.
  • For dedup, start deterministic; schedule fuzzy phases off‑peak.

11) Reliability Engineering

  • Idempotency: dedupe by event_id + updated_at.
  • Retries with jitter; circuit‑break noisy sources.
  • DLQ for poison data; add a replay CLI that tags correlation_id.
  • Backfills: run in read‑only mode for exports first; promote after QA.
  • Schema evolution: additive default; versioned breaking changes.

12) Security & Compliance (Engineer View)

  • Use least‑privilege tokens; scope to project/export.
  • Store secrets in a vault; never in repo or logs.
  • Respect labels: mask PII in staging and during AI prompts.
  • Use audit logs to evidence changes; link them in PRs.
  • Coordinate with Admins for role and policy changes.

13) AI Agents for Engineers

  • Ask Agents to profile data, suggest validations, and generate test cases.
  • Use Agents to propose mapping diffs and survivorship rules.
  • Restrict to masked views when fields carry PII/Restricted labels.
  • Keep auto‑fixes reversible; log before/after samples.

14) Collaboration Model

  • Stewards: define rules, thresholds, glossary; review DQ impacts.
  • Admins: SSO/roles, feature toggles, secrets, policies.
  • Engineers: implement, test, ship; own runtime health.

Change cadence: small PRs, clear runbooks, explicit rollback steps.

15) Runbooks (Common Incidents)

A) Ingestion 4xx/5xx spike

  1. Identify source + correlation_id.
  2. Check auth/quotas; inspect DLQ samples.
  3. Throttle producer or widen batch; hotfix schema parse.
  4. Reprocess DLQ after fix; confirm metrics normal.

B) Export schema drift

  1. Freeze prod export; route to staging.
  2. Diff mapping since last green; revert or bump v2.
  3. Notify consumers; run backfill if required.

C) Duplicate surge

  1. Raise auto‑approve threshold; pause auto‑merge.
  2. Add deterministic rule; schedule fuzzy off‑peak.
  3. Review precision/recall; promote new rules.

D) SLA breach (freshness)

  1. Identify slow stage (ingest, clean, export).
  2. Scale parallelism or adjust schedule.
  3. Add alert on pre‑cursor metrics (queue depth).

16) Templates & Snippets

16.1 Export Contract (YAML) 

name: contacts_v1
primary_key: contact_id
delivery:
  type: sql-table
  schedule: hourly
sla:
  freshness_minutes_p95: 60
schema:
  - name: contact_id
    type: string
    required: true
  - name: email
    type: string
  - name: updated_at
    type: timestamp
labels: ["PII:email"]

16.2 Policy Hook (mask PII for non-owners) 

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

16.3 Makefile Helpers 

lint:
\t./tools/validate-config.sh

deploy-staging:
\t./tools/apply.sh env/test

deploy-prod:
\t./tools/apply.sh env/prod

16.4 SQL Smoke Checks 

-- Row count sanity vs yesterday (±10%)
SELECT
  CASE WHEN ABS((today.c - yday.c) / NULLIF(yday.c,0)) <= 0.10 THEN 'OK' ELSE 'ALERT' END AS status
FROM
  (SELECT COUNT(*) c FROM mdm.contacts_v1 WHERE _load_date = CURRENT_DATE) today,
  (SELECT COUNT(*) c FROM mdm.contacts_v1 WHERE _load_date = CURRENT_DATE - INTERVAL '1 day') yday;

17) Operating Rhythm

Daily

  • Check pipeline health + last run metrics (2–5 min).
  • Triage DLQ; scan error logs by category.
  • Review DQ dashboard with Stewards.

Weekly

  • Ship 1–2 mapping/cleaning improvements.
  • Tighten tests for any incident class seen.
  • Cost/perf review: partitions, parallelism, batch sizes.

Monthly

  • Access & token review (with Admins).
  • Evaluate dedup thresholds (precision/recall).
  • Retire old export versions (_v0) after consumers migrate.

18) What “Good” Looks Like

  • Green end‑to‑end runs with alert noise < 1%.
  • Additive schema changes dominate; breaking changes are versioned.
  • DQ metrics improving quarter‑over‑quarter.
  • Runbooks exercised; MTTR trending down.
  • Config-as-code with clear history, rollbacks, and audit links.

You now have the Data Engineer blueprint: land data reliably, map minimally then iterate, clean & validate safely, export with contracts, test and observe everything, and collaborate tightly with Stewards and Admins. Ship small, ship often, and keep the pipeline healthy.