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- Published on
데이터 엔지니어링은 더 이상 "ETL 돌리는 일"이 아니다. 2025년의 데이터 엔지니어는 Lakehouse 아키텍처(Iceberg·Delta·Hudi)를 설계하고, Streaming(Flink·Kafka)과 Batch(Spark)를 결합하며, dbt로 데이터 모델링 표준화를 이끌고, Data Mesh로 조직 경계를 재정의하고, Data Contract로 팀 간 인터페이스를 관리한다. 이 글은 "데이터가 일하는 방식"이 달라진 2025년의 데이터 엔지니어링 한 편. Season 2의 여덟 번째.- Published on
Kafka를 쓰는 곳은 많지만 내부를 아는 사람은 드물다. 파티션과 Leader/ISR, Log Segment의 물리적 구조, Exactly-Once Semantics의 진실, Transactional Outbox, Event Sourcing vs CDC, Schema Registry, 100ms SLA 달성법까지.- Published on
Kafka의 내부 구조! 파티션과 세그먼트 파일, ISR(In-Sync Replica), Replication 메커니즘, KRaft (ZooKeeper 대체), Exactly-Once Semantics, Consumer Group rebalance, Tiered Storage, 운영 베스트 프랙티스.- Published on
메시지 큐와 이벤트 스트림의 근본 차이, Kafka 내부(파티션/세그먼트/ISR/KRaft), RabbitMQ의 유연한 라우팅, NATS JetStream의 가벼움, Pulsar의 지오리플리케이션, SQS/SNS/Kinesis/EventBridge 비교, Redis Streams의 실무, CDC(Debezium)의 실전, Avro/Protobuf 스키마 진화, Event-Driven의 함정까지. 이벤트가 흐르는 모든 구조.- Published on
마이크로서비스 통신의 모든 것! 동기 vs 비동기 패턴, REST vs gRPC vs GraphQL, Kafka 이벤트 드리븐, Saga 패턴(Choreography vs Orchestration), Outbox 패턴, 분산 트랜잭션, 멱등성, 회복탄력성 패턴(Circuit Breaker, Retry, Bulkhead).- Published on
Season 5 Ep 2. "모든 데이터를 실시간으로"는 2020년의 약속이었다. 2025년은 실용주의의 시대 — 배치와 스트리밍의 경계는 흐려졌고, 진짜 질문은 "이 데이터는 얼마나 신선해야 SLA를 만족하는가"로 바뀌었다. Flink·RisingWave·Materialize·ksqlDB 비교, Kappa/Lambda의 2025년 버전, Iceberg v3 row-level delete와 실시간 upsert, CDC 파이프라인, 비용·지연 트레이드오프, 한국 실무 사례까지.- Published on
Kafka가 초당 수백만 메시지를 처리하고 Netflix가 단일 서버로 수십 Gbps를 뿜어내는 비결, zero-copy I/O를 완전 분석한다. read/write, sendfile, splice, mmap, O_DIRECT까지 — 이론과 실전을 720줄로 깊이 있게 다룬다.- Published on
Event Streaming 심화! Kafka 내부(파티션/세그먼트/ISR/컨트롤러), Consumer Group(리밸런싱/오프셋 관리), Exactly-Once(트랜잭션/멱등성), Schema Registry(Avro/Protobuf 진화), Apache Flink(윈도우/워터마크/상태), Kafka Streams vs Flink.- Published on
데이터 파이프라인은 어떻게 구축하는가? ETL/ELT, 데이터 웨어하우스, 스트리밍, 배치 처리, Spark, Kafka, Airflow까지.- Published on
데이터 엔지니어링의 모든 것! ETL vs ELT, Apache Spark(PySpark), Apache Airflow(DAG/Operator/Sensor), 실시간 스트리밍(Kafka+Flink), dbt(데이터 변환), 데이터 웨어하우스(BigQuery/Snowflake/Redshift), 데이터 품질, 모니터링.- Published on
EDA 패턴의 모든 것! 도메인 이벤트, Event Sourcing(이벤트 저장소), CQRS(명령/조회 분리), Saga 패턴(Choreography vs Orchestration), Outbox 패턴, 멱등성, 최종 일관성, Kafka/RabbitMQ 실전 구현.- Published on
EAI(기업 애플리케이션 통합)의 모든 것을 한 글에! Hub-and-Spoke, ESB, API-Led, 이벤트 기반 아키텍처 패턴 비교, Gregor Hohpe의 Enterprise Integration Patterns, MuleSoft/Apache Camel/Dell Boomi 플랫폼 비교, 실전 ERP-CRM-WMS 연동 프로젝트까지.- Published on
Kafka 4.0 KRaft GA, RabbitMQ 4.2 스트림 필터링, Pulsar 4.1, NATS 2.11 — 2025년 메시지 큐 6종 완전 비교. 아키텍처, 벤치마크(Kafka 1M msgs/s vs RabbitMQ 50K), 가격, Spring Boot 연동까지 실전 선택 가이드.- Published on
Spring WebFlux의 리액티브 프로그래밍부터 Kafka 스트리밍, ZooKeeper에서 KRaft로의 전환, SSE/WebSocket/gRPC Streaming API까지 — 실시간 데이터 처리 아키텍처를 위한 개발자 완전 가이드. 벤치마크, 코드 예제, 아키텍처 패턴 총정리.- Published on
토스뱅크 Real-Time Data 팀의 Kafka & Streaming Data Engineer JD를 완전 분석합니다. Kafka Broker 운영, Spring Boot Kafka Client, Active-Active 이중화, CDC, Flink, ClickHouse까지 — 합격을 위한 기술스택 학습 로드맵과 면접 준비 전략.- Published on
Kafka와 RabbitMQ의 아키텍처 차이, LLM Gateway에서의 비동기 처리 패턴, 실전 구현 예제와 선택 가이드를 총정리합니다.- Published on
AI를 위한 데이터 엔지니어링 완전 가이드. Apache Spark, Kafka, Airflow, dbt, Delta Lake, 피처 스토어까지 대규모 데이터 파이프라인 설계와 구현을 다룹니다.- Published on
CAP theorem, Raft 합의 알고리즘, Kafka 메시지 큐부터 Ring-AllReduce 분산 ML 학습과 NCCL까지 AI 엔지니어를 위한 분산 시스템 완전 가이드입니다.- Published on
실시간 금융 데이터 파이프라인의 설계와 구현을 다룹니다. Kafka 기반 시장 데이터 수집, Flink 스트림 처리, CDC 연동, 윈도우 집계, 이상 거래 탐지까지 저지연 금융 데이터 아키텍처를 코드와 함께 구축합니다.- Published on
CQRS와 Event Sourcing의 핵심 원리부터 Kafka 기반 이벤트 스토어 구현, Saga 패턴을 통한 분산 트랜잭션 관리, 프로덕션 운영 노하우까지 다루는 종합 아키텍처 가이드.- Published on
이벤트 드리븐 아키텍처의 핵심 패턴을 실전 중심으로 다룹니다. Kafka 기반 메시징, CQRS, Event Sourcing의 개념과 구현을 코드 예제와 Mermaid 다이어그램으로 설명합니다.- Published on
MSA 환경에서 이벤트 드리븐 아키텍처, CQRS(Command Query Responsibility Segregation), Saga 패턴을 결합한 실전 설계. Kafka 기반 구현 코드, Mermaid 다이어그램, 그리고 주문-결제-배송 시스템 사례 포함.- Published on
Apache Kafka 설치 방법에 대해서 알아본다.(with Zookeeper)