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: data-eng-lakehouse
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title: Lakehouse — Iceberg / Delta / Parquet
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category: Coding
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status: draft
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source_trust_level: B
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verification_status: conceptual
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created_at: 2026-05-09
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updated_at: 2026-05-09
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tags: [data-engineering, lakehouse, iceberg, parquet, vibe-coding]
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tech_stack: { language: "SQL / Python", applicable_to: ["Data Engineering"] }
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applied_in: []
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aliases: [Apache Iceberg, Delta Lake, Hudi, Parquet, lakehouse, ACID on object storage]
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---
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# Lakehouse (Iceberg / Delta / Hudi)
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> Object storage (S3) + table format = warehouse 의 transaction + lake 의 cost. **Apache Iceberg = open standard, Delta Lake (Databricks), Hudi**. Spark / Trino / DuckDB / DataFusion 가 query.
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## 📖 핵심 개념
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- Parquet: 컬럼 binary format, 압축.
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- Table format: metadata layer — schema, snapshot, ACID.
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- Time travel: 옛 snapshot query.
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- Merge-on-Read vs Copy-on-Write.
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## 💻 코드 패턴
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### Parquet (기본 file format)
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```python
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import pandas as pd
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df = pd.DataFrame({'id': [1, 2, 3], 'name': ['a', 'b', 'c']})
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df.to_parquet('s3://bucket/data.parquet', engine='pyarrow', compression='zstd')
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# Read
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df = pd.read_parquet('s3://bucket/data.parquet')
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```
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→ Compression 자동, 컬럼 단위 read 가능.
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### Apache Iceberg (Spark)
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```python
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from pyspark.sql import SparkSession
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spark = SparkSession.builder \
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.config('spark.sql.extensions', 'org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions') \
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.config('spark.sql.catalog.cat', 'org.apache.iceberg.spark.SparkCatalog') \
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.config('spark.sql.catalog.cat.type', 'hadoop') \
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.config('spark.sql.catalog.cat.warehouse', 's3://bucket/warehouse') \
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.getOrCreate()
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# 테이블 생성
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spark.sql('''
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CREATE TABLE cat.db.orders (
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id BIGINT,
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user_id STRING,
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amount DECIMAL(10, 2),
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created_at TIMESTAMP
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) USING iceberg
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PARTITIONED BY (days(created_at))
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''')
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# Insert
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spark.sql("INSERT INTO cat.db.orders VALUES (1, 'u1', 99.50, '2026-05-09')")
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# Time travel
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spark.sql("SELECT * FROM cat.db.orders VERSION AS OF 12345")
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spark.sql("SELECT * FROM cat.db.orders TIMESTAMP AS OF '2026-05-01'")
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```
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### Iceberg with Trino / Athena / DuckDB
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```sql
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-- Trino
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CREATE TABLE iceberg.db.orders (...)
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WITH (format = 'PARQUET', partitioning = ARRAY['day(created_at)']);
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-- DuckDB (modern, lightweight)
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INSTALL iceberg;
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LOAD iceberg;
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SELECT * FROM iceberg_scan('s3://bucket/orders');
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```
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### Schema evolution
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```sql
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ALTER TABLE cat.db.orders ADD COLUMN status STRING;
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ALTER TABLE cat.db.orders RENAME COLUMN amount TO total;
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ALTER TABLE cat.db.orders DROP COLUMN status;
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```
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→ 옛 file 도 호환. 안전.
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### Partition evolution
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```sql
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ALTER TABLE cat.db.orders REPLACE PARTITION FIELD days(created_at) WITH hours(created_at);
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```
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→ 옛 data 그대로. 새 data 만 새 partition 으로.
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### Compaction (작은 file → 큰 file)
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```sql
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CALL cat.system.rewrite_data_files('db.orders');
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```
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→ Small file 문제 해결.
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### MERGE INTO (UPSERT)
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```sql
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MERGE INTO cat.db.orders t
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USING new_orders s
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ON t.id = s.id
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WHEN MATCHED THEN UPDATE SET *
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WHEN NOT MATCHED THEN INSERT *;
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```
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### Snapshot 관리
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```sql
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-- 옛 snapshot 만료 (storage 절약)
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CALL cat.system.expire_snapshots('db.orders', TIMESTAMP '2026-04-01');
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-- 옛 file 정리
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CALL cat.system.remove_orphan_files('db.orders');
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```
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### Delta Lake (Databricks 친화)
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```python
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from delta import configure_spark_with_delta_pip
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builder = SparkSession.builder.config(
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"spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtensions"
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).config(
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"spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog"
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)
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spark = configure_spark_with_delta_pip(builder).getOrCreate()
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spark.sql('CREATE TABLE db.orders (...) USING DELTA')
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spark.sql('SELECT * FROM db.orders VERSION AS OF 5')
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```
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```python
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# Python API
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from delta.tables import DeltaTable
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dt = DeltaTable.forPath(spark, '/path/to/orders')
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dt.alias('t').merge(
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new_data.alias('s'),
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't.id = s.id'
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).whenMatchedUpdateAll() \
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.whenNotMatchedInsertAll() \
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.execute()
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# Time travel
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df = spark.read.format('delta').option('versionAsOf', 5).load('/path')
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```
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### Iceberg vs Delta vs Hudi
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```
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Iceberg:
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+ 가장 open (Apache, vendor-neutral)
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+ Schema/partition evolution 강
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+ 큰 ecosystem (Snowflake, BigQuery, AWS, Trino)
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Delta Lake:
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+ Databricks native
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+ Modern features 빠름
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- Open source 정도 (DI 전체 X)
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Hudi:
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+ Streaming 친화
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+ Merge-on-Read 강
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- 작은 community (vs Iceberg)
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```
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→ **2026 현재 = Iceberg 가 표준 추세**.
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### Streaming → Lakehouse
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```python
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# Spark Structured Streaming
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stream = spark.readStream.format('kafka').option(...).load()
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parsed = stream.selectExpr('CAST(value AS STRING) as json').select(from_json('json', schema).alias('d'))
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flat = parsed.select('d.*')
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flat.writeStream \
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.format('iceberg') \
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.outputMode('append') \
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.option('path', 'cat.db.events') \
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.option('checkpointLocation', 's3://checkpoints/events') \
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.trigger(processingTime='1 minute') \
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.start()
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```
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→ Real-time → Iceberg.
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### CDC ingestion (Debezium → Iceberg)
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```
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DB → Debezium → Kafka → Spark / Flink → Iceberg
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```
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### File layout
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```
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s3://bucket/warehouse/db/orders/
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├── data/
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│ ├── year=2026/month=05/day=09/file-uuid.parquet
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│ └── ...
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└── metadata/
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├── snap-xxx.avro (snapshot)
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├── manifest-yyy.avro (manifest list)
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└── v1.metadata.json (version pointer)
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```
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### Catalog (REST / Hive / Glue / Nessie)
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```
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Hive Metastore — legacy
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AWS Glue — AWS native
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REST catalog — Iceberg 표준
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Nessie — git-like branching
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Polaris — open
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Tabular — managed
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```
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```python
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# Nessie — branch / merge
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spark.sql("CREATE BRANCH dev IN cat FROM main")
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spark.sql("USE REFERENCE dev IN cat")
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# Dev 환경 — production 영향 X
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```
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### Cost
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```
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S3 storage: $23/TB/month (Standard)
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Glacier: $4/TB/month (cold)
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vs warehouse:
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Snowflake: $40+/TB/month (compute 별도)
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BigQuery: $20/TB/month + $6.25/TB query
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```
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→ Lakehouse = 큰 cost 절감.
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### Compute engines
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```
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Spark: 표준 batch
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Flink: streaming
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Trino: interactive query
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DuckDB: single-node, fast
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DataFusion: Rust, embeddable
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Snowflake / BigQuery: 외부 catalog 통해 query
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```
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## 🤔 의사결정 기준
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| 상황 | 추천 |
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|---|---|
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| 새 lake | Iceberg |
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| Databricks | Delta Lake |
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| Streaming heavy | Hudi 또는 Iceberg + Flink |
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| 작은 / 단일 노드 | DuckDB + Parquet |
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| Compute analytic | Trino / Spark |
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| Managed | Snowflake / BigQuery / Databricks |
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## ❌ 안티패턴
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- **CSV / JSON prod**: parse 비싸, schema 약함. Parquet.
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- **작은 file 많음**: query slow. Compaction.
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- **Partition 너무 잘게**: 너무 많은 file.
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- **Snapshot expire 안 함**: storage 폭발.
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- **Schema 무관 INSERT**: 깨짐. enforce.
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- **Direct S3 write 동기화 X**: race. transactional.
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- **Catalog 없음 — file path 직접**: schema 추적 안 됨.
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## 🤖 LLM 활용 힌트
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- Iceberg + S3 + Trino/Spark 가 modern OSS stack.
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- Catalog (Glue / Nessie / Polaris).
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- Compaction + snapshot expire 정기.
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## 🔗 관련 문서
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- [[Data_Eng_dbt]]
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- [[Data_Eng_Airflow_Dagster]]
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- [[DB_ClickHouse_OLAP]]
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