refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-snowflake-data-warehousing
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title: Snowflake Data Warehousing
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Snowflake, Snowflake DW, Snowflake Cloud Data Platform]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [database, data-warehouse, cloud, analytics]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: sql
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framework: snowflake
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---
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# Snowflake Data Warehousing
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## 매 한 줄
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> **"매 storage 매 separated · 매 compute 매 elastic"**. Snowflake는 매 multi-cluster shared-data architecture 의 cloud DW — micro-partition columnar storage · virtual warehouse · zero-copy clone · time travel · Iceberg 매 native(2026). Databricks · BigQuery · Redshift 매 big-3 경쟁.
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## 매 핵심
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### 매 Architecture (3 layers)
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- **Storage**: S3/GCS/Blob 매 micro-partitions(50–500MB), columnar(FDN). 매 compressed.
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- **Compute (Virtual Warehouses)**: independent compute clusters, X-Small ~ 6X-Large. 매 per-second billed.
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- **Cloud Services**: metadata · query optimization · auth · 매 stateless brain.
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### 매 Key features
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- **Zero-copy clone**: instant DB/schema/table copy via metadata.
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- **Time Travel**: query as of 90-day past (Enterprise: 90, default 1).
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- **Streams + Tasks**: CDC + scheduled SQL = native pipeline.
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- **Snowpark**: Python/Scala/Java in-DB compute.
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- **Iceberg tables (2026)**: external open-table format.
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- **Cortex AI**: built-in LLM functions.
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### 매 응용
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1. Analytical workloads (OLAP, BI).
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2. Data sharing (Secure Data Share — no copy).
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3. ELT with dbt.
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4. ML feature engineering (Snowpark + Cortex).
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## 💻 패턴
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### Warehouse sizing & auto-suspend
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```sql
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CREATE WAREHOUSE etl_wh
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WAREHOUSE_SIZE = 'MEDIUM'
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AUTO_SUSPEND = 60
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AUTO_RESUME = TRUE
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MIN_CLUSTER_COUNT = 1
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MAX_CLUSTER_COUNT = 4
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SCALING_POLICY = 'STANDARD';
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```
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### Copy from S3 (bulk load)
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```sql
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CREATE STAGE my_stage URL='s3://bucket/path/' STORAGE_INTEGRATION = my_int;
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COPY INTO orders
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FROM @my_stage/orders/
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FILE_FORMAT = (TYPE = PARQUET)
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MATCH_BY_COLUMN_NAME = CASE_INSENSITIVE
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ON_ERROR = 'CONTINUE';
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```
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### Zero-copy clone for testing
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```sql
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-- 매 instant · 매 storage 추가 X (copy-on-write)
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CREATE DATABASE prod_clone CLONE prod;
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-- 매 dbt CI 매 패턴
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```
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### Time travel + undrop
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```sql
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SELECT * FROM orders AT (OFFSET => -60*5); -- 5분 전
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SELECT * FROM orders BEFORE (STATEMENT => '01a...');
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UNDROP TABLE orders; -- 매 within retention
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```
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### Streams + Tasks (CDC pipeline)
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```sql
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CREATE STREAM orders_stream ON TABLE orders;
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CREATE TASK orders_etl
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WAREHOUSE = etl_wh
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SCHEDULE = '5 MINUTE'
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WHEN SYSTEM$STREAM_HAS_DATA('orders_stream')
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AS
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INSERT INTO orders_silver
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SELECT *, CURRENT_TIMESTAMP() AS ingest_ts
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FROM orders_stream;
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ALTER TASK orders_etl RESUME;
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```
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### Snowpark Python (in-DB compute)
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```python
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from snowflake.snowpark import Session, functions as F
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sess = Session.builder.configs(cfg).create()
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df = sess.table('orders') \
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.filter(F.col('amount') > 100) \
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.group_by('customer_id') \
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.agg(F.sum('amount').alias('total'))
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df.write.save_as_table('top_customers', mode='overwrite')
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```
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### Cortex AI (LLM in SQL)
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```sql
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SELECT order_id,
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SNOWFLAKE.CORTEX.SUMMARIZE(review_text) AS summary,
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SNOWFLAKE.CORTEX.SENTIMENT(review_text) AS sentiment
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FROM reviews;
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-- Free-text classify
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SELECT SNOWFLAKE.CORTEX.CLASSIFY_TEXT(
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ticket_body,
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['billing','technical','refund','other']
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) FROM tickets;
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```
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### Iceberg external table (2026)
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```sql
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CREATE ICEBERG TABLE events
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CATALOG = my_glue
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EXTERNAL_VOLUME = my_s3_vol
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CATALOG_TABLE_NAME = 'analytics.events';
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-- 매 Snowflake/Spark/Trino 매 same data.
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```
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### Cost optimization (Resource Monitor)
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```sql
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CREATE RESOURCE MONITOR rm_dev
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WITH CREDIT_QUOTA = 100
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TRIGGERS
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ON 80 PERCENT DO NOTIFY
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ON 100 PERCENT DO SUSPEND;
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ALTER WAREHOUSE etl_wh SET RESOURCE_MONITOR = rm_dev;
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```
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## 매 결정 기준
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| 상황 | Choice |
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|---|---|
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| BI / dashboards | Snowflake + dbt |
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| Open lakehouse | Iceberg + Snowflake/Databricks |
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| Spark-heavy ML | Databricks |
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| GCP-native | BigQuery |
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| Sub-second OLAP | ClickHouse / Druid |
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| Tiny data <100GB | Postgres + DuckDB |
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**기본값**: Snowflake + dbt + Iceberg (open + managed).
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## 🔗 Graph
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- 부모: [[Data Warehouse]] · [[Cloud Native]]
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- 변형: [[ClickHouse]]
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- 응용: [[Feature Store]]
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- Adjacent: [[Apache Iceberg]] · [[dbt]] · [[Principles of Data Connect]]
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## 🤖 LLM 활용
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**언제**: SQL tuning suggestion, dbt model scaffolding, Cortex function selection.
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**언제 X**: production query 매 직접 실행 — 매 EXPLAIN + governance review.
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## ❌ 안티패턴
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- **Always-on warehouse**: AUTO_SUSPEND 미설정 → cost 폭발.
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- **SELECT * on wide table**: columnar 의 이점 매 손실.
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- **One huge warehouse**: workload isolation X — ETL 매 BI 매 contend.
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- **No clustering on huge table**: prune 매 작동 X — full scan.
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- **Copy data instead of Data Share**: governance · cost penalty.
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## 🧪 검증 / 중복
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- Verified (Snowflake docs 2026; Dageville et al. SIGMOD 2016; *Snowflake: The Definitive Guide* 2nd ed).
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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 — full content (architecture + 9 patterns) |
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