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Antigravity Agent
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id: wiki-2026-0508-real-time-data-streaming
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title: Real-time Data Streaming
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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: [Stream Processing, Event Streaming, Real-time Pipelines]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.95
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verification_status: applied
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tags: [streaming, kafka, pulsar, flink, data-engineering]
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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: kafka-flink-pulsar
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---
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# Real-time Data Streaming
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## 매 한 줄
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> **"매 batch ETL 의 X — 매 unbounded events 매 milliseconds latency 매 process"**. Kafka (LinkedIn 2010) → Flink / Spark Structured Streaming / Pulsar / Materialize / RisingWave 매 modern stack. 매 2026 매 sub-second analytics 매 default.
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## 매 핵심
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### 매 layers
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- **Ingest**: Kafka, Pulsar, Kinesis, Redpanda — 매 durable log.
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- **Process**: Flink, Spark Streaming, Kafka Streams, Bytewax, Arroyo.
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- **Serve**: Materialize, RisingWave, Pinot, Druid, ClickHouse.
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### 매 windowing
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- **Tumbling**: fixed, non-overlapping (1-min buckets).
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- **Sliding**: overlapping (5-min, slide 1-min).
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- **Session**: gap-based (user activity until 30s inactivity).
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- **Hopping**: same as sliding (different name).
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### 매 time semantics
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- **Event time**: 매 actual occurrence — 매 correctness 위해 default.
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- **Processing time**: 매 broker arrival — 매 latency 측정.
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- **Ingestion time**: 매 broker append.
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- **Watermarks**: 매 lateness threshold — Flink/Beam 핵심.
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### 매 응용
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1. Fraud detection — 매 payment stream + ML inference.
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2. Real-time dashboards — Materialize + Grafana.
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3. CDC pipelines — Debezium → Kafka → warehouse.
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4. IoT telemetry — MQTT → Kafka → anomaly detection.
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5. Personalization — clickstream → feature store → model.
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## 💻 패턴
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### Kafka Streams (Python via Faust / Bytewax)
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```python
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import bytewax.operators as op
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from bytewax.dataflow import Dataflow
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from bytewax.connectors.kafka import KafkaSource, KafkaSink
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flow = Dataflow("fraud")
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src = op.input("in", flow, KafkaSource(["localhost:9092"], ["payments"]))
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parsed = op.map("parse", src, lambda kv: json.loads(kv.value))
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flagged = op.filter("flag", parsed, lambda p: p["amount"] > 10000)
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op.output("out", flagged, KafkaSink(["localhost:9092"], "alerts"))
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```
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### Flink SQL (windowed aggregation)
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```sql
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CREATE TABLE clicks (
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user_id STRING, ts TIMESTAMP(3),
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WATERMARK FOR ts AS ts - INTERVAL '5' SECOND
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) WITH ('connector' = 'kafka', ...);
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SELECT
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user_id,
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TUMBLE_START(ts, INTERVAL '1' MINUTE) AS w_start,
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COUNT(*) AS clicks
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FROM clicks
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GROUP BY user_id, TUMBLE(ts, INTERVAL '1' MINUTE);
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```
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### Materialize (streaming SQL view)
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```sql
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CREATE SOURCE orders FROM KAFKA BROKER 'kafka:9092' TOPIC 'orders'
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FORMAT AVRO USING SCHEMA REGISTRY 'http://sr:8081';
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CREATE MATERIALIZED VIEW revenue_5min AS
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SELECT
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date_trunc('minute', ts) AS minute,
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SUM(amount) AS revenue
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FROM orders
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WHERE ts > now() - INTERVAL '5 minutes'
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GROUP BY 1;
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-- Subscribe to changes
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SUBSCRIBE TO revenue_5min;
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```
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### Spark Structured Streaming
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```python
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df = (spark.readStream.format("kafka")
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.option("subscribe", "events")
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.load())
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agg = (df.selectExpr("CAST(value AS STRING) as json")
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.select(from_json("json", schema).alias("e"))
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.withWatermark("e.ts", "10 minutes")
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.groupBy(window("e.ts", "1 minute"), "e.user")
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.count())
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agg.writeStream.format("delta").outputMode("append").start("/lake/agg")
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```
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### Pulsar Functions (lightweight processing)
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```python
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from pulsar import Function
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class EnrichOrder(Function):
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def process(self, msg, ctx):
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order = json.loads(msg)
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order["region"] = lookup_region(order["zip"])
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ctx.publish("orders.enriched", json.dumps(order))
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```
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### Exactly-once with Kafka transactions
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```python
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producer = KafkaProducer(transactional_id="tx-1", enable_idempotence=True)
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producer.init_transactions()
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try:
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producer.begin_transaction()
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producer.send("out", value=processed)
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producer.send_offsets_to_transaction(offsets, group_id)
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producer.commit_transaction()
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except:
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producer.abort_transaction()
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```
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### CDC with Debezium
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```yaml
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# connector config
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connector.class: io.debezium.connector.postgresql.PostgresConnector
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database.hostname: pg
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table.include.list: public.orders
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plugin.name: pgoutput
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# emits CDC events to Kafka topic "pg.public.orders"
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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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| 매 simple transform | Kafka Streams / Bytewax |
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| 매 complex windowing, joins | Flink |
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| 매 SQL-first analytics | Materialize / RisingWave |
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| 매 batch+stream unified | Spark Structured / Beam |
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| 매 lightweight, serverless | Pulsar Functions / AWS Lambda |
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| 매 OLAP serving | Pinot / Druid / ClickHouse |
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**기본값**: 매 2026 매 SQL-on-streams (Materialize/RisingWave) 매 default — DX 압도적.
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## 🔗 Graph
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- 부모: [[Data Engineering]] · [[Event-Driven Architecture]]
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- 변형: [[Stream-Processing-Architectures|Stream Processing]] · [[CEP]]
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- 응용: [[CDC]]
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- Adjacent: [[Kafka]] · [[Materialize]]
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## 🤖 LLM 활용
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**언제**: 매 SQL DDL/query generation, 매 schema evolution analysis, 매 anomaly investigation summarization.
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**언제 X**: 매 latency-critical hot path — LLM inference 매 too slow. 매 trained ML model 사용.
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## ❌ 안티패턴
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- **Processing time everywhere**: 매 out-of-order events 매 wrong results — event time + watermarks 사용.
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- **Unbounded state**: 매 keyed state 매 grows forever — TTL / windows 필수.
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- **Tiny files**: 매 1 record / file → S3 explosion. 매 batching + compaction.
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- **Sync external calls in pipeline**: 매 backpressure 폭발. 매 async + bulkhead.
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- **No replay strategy**: 매 bad code → poisoned downstream. 매 reset offset + idempotent sinks.
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## 🧪 검증 / 중복
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- Verified (Akidau — "Streaming 101/102"; Kafka docs; Flink docs 1.18+).
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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 streaming entry with Materialize/RisingWave |
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