Antigravity Agent
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10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
168 lines
5.4 KiB
Markdown
168 lines
5.4 KiB
Markdown
---
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id: wiki-2026-0508-big-data
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title: Big Data
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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: [Big Data, Large-Scale Data Processing]
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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: [data, distributed-systems, analytics, lakehouse]
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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: python
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framework: spark,duckdb,iceberg,polars
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---
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# Big Data
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## 매 한 줄
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> **"매 single-machine 의 fit 의 X · single-pass 의 fit 의 X — 매 distributed compute · columnar storage 의 require"**. 2003 Google MapReduce 논문 의 origin, Hadoop → Spark → Lakehouse (Iceberg+Delta+Hudi) 의 evolve, 2026 의 single-node DuckDB/Polars 의 "Big Data is dead" 의 movement 의 mainstream.
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## 매 핵심
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### 매 5 V
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- **Volume**: TB-PB scale.
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- **Velocity**: streaming · near-real-time.
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- **Variety**: structured + semi + unstructured.
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- **Veracity**: data quality · trust.
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- **Value**: ROI of analytics.
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### 매 stack 2026
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- **Storage**: object store (S3/GCS) + open table format (Iceberg · Delta · Hudi).
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- **Compute**: Spark · Trino · DuckDB · Polars · Snowflake · BigQuery.
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- **Orchestration**: Airflow · Dagster · Prefect.
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- **Stream**: Kafka · Flink · Kinesis.
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- **Catalog**: Unity · Polaris · Nessie · Glue.
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### 매 응용
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1. 매 BI 의 dashboard.
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2. ML training pipeline (feature store).
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3. Operational analytics (real-time fraud, ad bidding).
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## 💻 패턴
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### Iceberg table 의 Spark 에서 의 write
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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.catalog.local", "org.apache.iceberg.spark.SparkCatalog") \
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.config("spark.sql.catalog.local.type", "hadoop") \
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.config("spark.sql.catalog.local.warehouse", "s3a://lake/warehouse") \
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.getOrCreate()
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df = spark.read.json("s3a://raw/events/*.json")
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df.writeTo("local.events.daily").partitionedBy("date").createOrReplace()
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```
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### DuckDB: 매 single-node "big data" (laptop 의 100GB)
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```python
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import duckdb
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con = duckdb.connect()
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con.sql("""
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SELECT user_id, COUNT(*) AS events, SUM(amount) AS revenue
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FROM read_parquet('s3://lake/events/2026/*.parquet')
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WHERE event_type = 'purchase'
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GROUP BY user_id
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ORDER BY revenue DESC
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LIMIT 100
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""").show()
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```
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### Polars: out-of-core lazy
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```python
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import polars as pl
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df = (
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pl.scan_parquet("s3://lake/events/*.parquet")
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.filter(pl.col("event_type") == "purchase")
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.group_by("user_id")
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.agg(pl.len().alias("events"), pl.col("amount").sum().alias("revenue"))
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.sort("revenue", descending=True)
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.limit(100)
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)
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print(df.collect(streaming=True))
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```
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### Flink: streaming aggregation
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```java
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DataStream<Event> events = env.fromSource(kafkaSource, ...);
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events.keyBy(Event::userId)
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.window(TumblingEventTimeWindows.of(Time.minutes(5)))
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.aggregate(new RevenueAggregator())
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.sinkTo(icebergSink);
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```
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### Iceberg time-travel + schema evolution
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```sql
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-- snapshot 의 query
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SELECT * FROM events FOR VERSION AS OF 8723649283746;
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SELECT * FROM events FOR TIMESTAMP AS OF '2026-05-09 00:00:00';
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-- column add (no rewrite)
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ALTER TABLE events ADD COLUMN device_id STRING;
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-- partition evolution
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ALTER TABLE events ADD PARTITION FIELD bucket(16, user_id);
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```
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### Spark: dynamic partition pruning + AQE
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```python
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spark.conf.set("spark.sql.adaptive.enabled", "true")
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spark.conf.set("spark.sql.adaptive.coalescePartitions.enabled", "true")
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spark.conf.set("spark.sql.adaptive.skewJoin.enabled", "true")
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# AQE 의 plan 의 runtime 의 reoptimize
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df = spark.sql("""
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SELECT u.name, e.event_type, COUNT(*)
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FROM events e JOIN users u ON e.user_id = u.id
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WHERE e.date >= '2026-05-01'
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GROUP BY u.name, e.event_type
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""")
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```
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## 매 결정 기준
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| 상황 | Stack |
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|---|---|
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| < 100 GB · single node | DuckDB · Polars |
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| 100GB - 10TB batch | Spark + Iceberg |
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| > 10 TB / day | Spark/Trino + Iceberg + Snowflake |
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| Streaming < 1s latency | Flink + Kafka |
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| Ad-hoc SQL | Trino · DuckDB |
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| ML training | Spark + Petastorm 또는 Ray Data |
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**기본값**: Iceberg-on-S3 + Spark/DuckDB 의 hybrid — 매 modern lakehouse 의 standard.
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## 🔗 Graph
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- 부모: [[Distributed Systems]] · [[Data Engineering]]
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- 변형: [[Lakehouse]] · [[Data Warehouse]]
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- 응용: [[Apache Ignite]]
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- Adjacent: [[Append-only log]] · [[Stream-Processing-Architectures|Stream Processing]]
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## 🤖 LLM 활용
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**언제**: SQL 의 generate, partitioning strategy 의 advise, schema evolution diff 의 explain, Iceberg 의 table maintenance 의 query 의 draft.
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**언제 X**: 매 production tuning (shuffle partition · executor sizing) — metric-driven 의 require, LLM hint 의 starting point 만.
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## ❌ 안티패턴
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- **Premature distribution**: 매 < 50GB 의 case 의 Spark — DuckDB 의 100x faster.
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- **Small file problem**: Spark 의 1KB parquet 의 millions — compaction 의 require.
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- **Hive-style partition explosion**: 매 high-cardinality column 의 partition (e.g. user_id) — Iceberg bucket transform 의 use.
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- **Schema-on-read 의 over-rely**: governance 의 erode — open table format 의 use.
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## 🧪 검증 / 중복
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- Verified (Apache Iceberg/Spark/Flink docs, MotherDuck "Big Data is Dead" 2023, Databricks Lakehouse paper).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — 5V + 2026 lakehouse stack + DuckDB/Polars/Flink patterns |
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