[G1-Sync] Manual knowledge update
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Antigravity Agent
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id: wiki-2026-0508-principles-of-data-connect
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title: Principles of Data Connect
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category: 10_Wiki/Topics
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status: merged
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redirect_to: 데이터_엔지니어링_및_가상_인프라_표준
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canonical_id: wiki-2026-0508-001
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aliases: []
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status: verified
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canonical_id: self
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aliases: [Data Integration Principles, ETL Design]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.92
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tags: [uncategorized]
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confidence_score: 0.85
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verification_status: applied
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tags: [data-engineering, etl, integration]
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raw_sources: []
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last_reinforced: 2026-05-08
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last_reinforced: 2026-05-10
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github_commit: pending
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inferred_by: Claude Opus 4.7 (auto-normalize 2026-05-08)
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tech_stack:
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language: unspecified
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framework: unspecified
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language: Python
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framework: dbt
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---
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# Redirect
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# Principles of Data Connect
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이 문서는 Canonical 문서인 [[데이터_엔지니어링_및_가상_인프라_표준]]으로 통합되었습니다.
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모든 최신 지식과 세부 내용은 위 링크를 참조하십시오.
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## 매 한 줄
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> **"매 source-to-warehouse 의 reliable pipe 의 design rules"**. 매 Inmon (1990s warehouse) → 매 Kimball (star schema) → 매 modern data stack (Fivetran/Airbyte → Snowflake/BigQuery → dbt) 의 evolution 의 distilled principles.
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## 매 핵심
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### 매 the principles
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1. **Idempotent loads** — re-run produces same result.
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2. **Schema-on-read tolerance** — handle source schema drift.
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3. **Replayability** — store raw, transform downstream.
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4. **Incremental + full-refresh** — both modes supported.
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5. **Observability** — row counts, freshness, anomaly alerts.
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6. **Lineage** — every column traces to source.
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7. **Privacy / PII** — masked or never-pulled.
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### 매 modern stack (2026)
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- Extract-Load: Fivetran, Airbyte, Stitch.
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- Warehouse: Snowflake, BigQuery, Databricks.
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- Transform: dbt (most-prevalent), Coalesce, SQLMesh.
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- Orchestrate: Airflow, Dagster, Prefect.
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- Observability: Monte Carlo, Datafold, Elementary.
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### 매 응용
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1. Analytics (BI dashboards).
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2. ML feature stores.
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3. Reverse-ETL to operational tools (Hightouch, Census).
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## 💻 패턴
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### Idempotent upsert (MERGE)
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```sql
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MERGE INTO dim_customer t
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USING staging_customer s
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ON t.customer_id = s.customer_id
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WHEN MATCHED AND s.updated_at > t.updated_at THEN UPDATE SET ...
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WHEN NOT MATCHED THEN INSERT (...) VALUES (...);
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```
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### dbt incremental model
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```sql
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{{ config(materialized='incremental', unique_key='order_id', on_schema_change='append_new_columns') }}
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select *
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from {{ source('raw', 'orders') }}
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{% if is_incremental() %}
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where _ingested_at > (select max(_ingested_at) from {{ this }})
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{% endif %}
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```
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### Schema-on-read (raw landing)
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```sql
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-- raw zone: VARIANT / JSON column, no schema enforcement
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CREATE TABLE raw.events (
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_ingested_at TIMESTAMP,
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_source STRING,
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payload VARIANT
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);
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-- bronze: typed extraction
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CREATE VIEW bronze.events AS
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SELECT _ingested_at, payload:event_type::STRING AS event_type, ...
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FROM raw.events;
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```
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### Data quality test (dbt)
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```yaml
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# models/marts/orders.yml
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version: 2
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models:
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- name: dim_orders
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columns:
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- name: order_id
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tests: [not_null, unique]
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- name: total_amount
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tests:
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- not_null
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- dbt_expectations.expect_column_values_to_be_between:
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min_value: 0
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max_value: 1000000
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```
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### Lineage (dbt-generated graph)
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```bash
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dbt docs generate
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dbt docs serve # column-level lineage in browser
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```
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### PII masking on load
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```sql
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CREATE OR REPLACE MASKING POLICY email_mask AS (val STRING) RETURNS STRING ->
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CASE WHEN CURRENT_ROLE() IN ('ANALYTICS_ADMIN') THEN val
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ELSE REGEXP_REPLACE(val, '.+@', '***@') END;
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ALTER TABLE customers MODIFY COLUMN email SET MASKING POLICY email_mask;
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```
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### Freshness SLA (dbt)
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```yaml
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sources:
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- name: stripe
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freshness:
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warn_after: { count: 1, period: hour }
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error_after: { count: 6, period: hour }
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loaded_at_field: _ingested_at
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```
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## 매 결정 기준
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| Need | Tool |
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|---|---|
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| SaaS source ingestion | Fivetran / Airbyte |
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| Transform | dbt |
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| Orchestration | Dagster (modern) / Airflow (mature) |
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| Observability | Monte Carlo / Elementary |
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| Reverse ETL | Hightouch / Census |
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**기본값**: Fivetran → Snowflake → dbt → Hightouch + dbt-tests + Elementary.
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## 🔗 Graph
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- 부모: [[Data-Engineering]]
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- 변형: [[ETL]] · [[ELT]] · [[Reverse-ETL]]
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- 응용: [[Data-Warehousing]] · [[Feature-Store]]
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- Adjacent: [[dbt]] · [[Snowflake-Data-Warehousing]] · [[Airflow]]
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## 🤖 LLM 활용
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**언제**: data-pipeline design, ETL architecture review, warehouse migration.
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**언제 X**: streaming-only / event-driven systems (use Kafka patterns instead).
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## ❌ 안티패턴
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- **Transform-on-extract**: 매 lose replay capability.
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- **No idempotency**: re-runs corrupt warehouse.
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- **Untested models**: 매 silent breakage.
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- **PII in raw zone unmasked**: compliance risk.
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## 🧪 검증 / 중복
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- Verified (Kimball — Data Warehouse Toolkit; Modern Data Stack docs; dbt best practices).
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- 신뢰도 A-.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Data Connect FULL with modern data stack patterns |
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