Apache Kafka® 소개
Apache Kafka is an open-source distributed streaming system used for stream processing, real-time data pipelines, and data integration at scale. Originally created to handle real-time data feeds at LinkedIn in 2011, Kafka quickly evolved from a messaging queue to a full-fledged event streaming platform, capable of handling over one million messages per second, or trillions of messages per day.
Founded by the original creators of Apache Kafka, Confluent provides the most comprehensive Kafka tutorials, training, services, and support. Confluent also offers fully managed, cloud-native data streaming services built for any cloud environment, ensuring scalability and reliability for modern data infrastructure needs.
왜 Kafka를 선택해야 할까요?
Kafka에는 수많은 이점이 있습니다. 오늘날 Kafka는 거의 모든 산업에서 포춘지 선정 100대 기업 중 80% 이상이 크고 작은 수많은 사용 사례에 사용하고 있습니다. 개발자 및 아키텍트가 최신 세대의 확장 가능한 실시간 데이터 스트리밍 애플리케이션을 구축하는 데 사용하고 있는 사실상의 표준(de facto) 기술입니다. 이러한 작업은 시장에서 사용할 수 있는 다양한 기술로 수행할 수 있지만 Kafka가 이처럼 널리 사용되는 주된 이유는 다음과 같습니다.
High Throughput
Kafka is capable of handling high-velocity and high-volume data, processing millions of messages per second. This makes it ideal for applications requiring real-time data processing and integration across multiple servers.
High Scalability
Kafka clusters can be scaled up to a thousand brokers, handling trillions of messages per day and petabytes of data. Kafka's partitioned log model allows for elastic expansion and contraction of storage and processing capacities. This scalability ensures that Kafka can support a vast array of data sources and streams.
Low Latency
Kafka can deliver a high volume of messages using a cluster of machines with latencies as low as 2ms. This low latency is crucial for applications that require real-time data processing and immediate responses to data streams.
Permanent Storage
Kafka safely and securely stores streams of data in a distributed, durable, and fault-tolerant cluster. This ensures that data records are reliably stored and can be accessed even in the event of server failure. The partitioned log model further enhances Kafka's ability to manage data streams and provide exactly-once processing guarantees.
High Availability
Kafka can extend clusters efficiently over availability zones, or connect clusters across geographic regions. This high availability makes Kafka fault-tolerant with no risk of data loss. Kafka’s design allows it to manage multiple subscribers and external stream processing systems seamlessly.
How Does Apache Kafka Work?
Apache Kafka consists of a storage layer and a compute layer, which enable efficient, real-time data ingestion, streaming data pipelines, and storage across distributed systems. Its design facilitates simplified data streaming between Kafka and external systems, so you can easily manage real-time data and scale within any type of infrastructure.
대규모 실시간 처리
A data streaming platform would not be complete without the ability to process and analyze data as soon as it's generated. The Kafka Streams API is a powerful, lightweight library that allows for on-the-fly processing, letting you aggregate, create windowing parameters, perform joins of data within a stream, and more. It is built as a Java application on top of Kafka, which maintains workflow continuity without requiring extra clusters to manage.
내구성이 뛰어난 영구 스토리지
분산 데이터베이스에서 일반적으로 찾을 수 있는 분산 커밋 로그의 추상화인 Apache Kafka는 내구성이 뛰어난 스토리지를 제공합니다. Kafka는 '정보 소스' 역할을 하여, 단일 데이터 센터 내 또는 여러 가용성 영역 전반에서 가용성이 뛰어난 배포를 위해 여러 노드에 데이터를 분산할 수 있습니다.
발행, 구독
중심에 위치한 작고 변경 불가능한 커밋 로그를 통해 구독을 수행하고 원하는 수의 시스템 또는 실시간 애플리케이션으로 데이터를 발행할 수 있습니다. 메시징 큐와 달리 Kafka는 고도로 확장 가능한 내결함성 분산 시스템으로, Uber에서의 승객 및 운전자 매칭 관리, British Gas의 스마트 홈을 위한 실시간 분석 및 예측 유지보수 제공, LinkedIn에서의 수많은 실시간 서비스 실행과 같은 애플리케이션에 배포할 수 있습니다. 이 고유한 성능으로 인해 하나의 앱에서 전사적 용도로 확장하기에 적합합니다.
What is Kafka Used For?
Commonly used to build real-time streaming data pipelines and real-time streaming applications, Kafka supports a vast array of use cases. Any company that relies on, or works with data, can find numerous benefits in utilizing Kafka.
Data Pipelines
In the context of Apache Kafka, a streaming data pipeline means ingesting the data from sources into Kafka as it’s created, and then streaming that data from Kafka to one or more targets. This allows for seamless data integration and efficient data flow across different systems.
Stream Processing
Stream processing includes operations like filters, joins, maps, aggregations, and other transformations that enterprises leverage to power many use cases. Kafka Streams, a stream processing library built for Apache Kafka, enables enterprises to process data in real-time, making it ideal for applications requiring immediate data processing and analysis.
Streaming Analytics
Kafka provides high throughput event delivery. When combined with open-source technologies such as Druid, it can form a powerful Streaming Analytics Manager (SAM). Druid consumes streaming data from Kafka to enable analytical queries. Events are first loaded into Kafka, where they are buffered in Kafka brokers, then they are consumed by Druid real-time workers. This allows for real-time analytics and decision-making.
Streaming ETL
Real-time ETL with Kafka combines different components and features such as Kafka Connect source and sink connectors, used to consume and produce data from/to any other database, application, or API; Single Message Transforms (SMT)—an optional Kafka Connect feature; and Kafka Streams for continuous data processing in real-time at scale. Altogether they ensure efficient data transformation and integration.
Event-Driven Microservices
Apache Kafka is the most popular tool for microservices, because it solves many issues related to microservices orchestration, while enabling attributes that microservices aim to achieve, such as scalability, efficiency, and speed. Kafka also facilitates inter-service communication, preserving ultra-low latency and fault tolerance. This makes it essential for building robust and scalable microservices architectures.
By using Kafka's capabilities, organizations can build highly efficient data pipelines, process streams of data in real time, perform advanced analytics, and develop scalable microservices—all ensuring they can meet the demands of modern data-driven applications.
Apache Kafka in Action
Kafka를 사용하는 기업
Some of the world’s biggest brands use Kafka:
To Maximize Kafka, You Need Confluent
Founded by the original developers of Kafka, Confluent delivers the most complete distribution of Kafka, improving Kafka with additional community and commercial features designed to enhance the streaming experience of both operators and developers in production, at massive scale.
You love Apache Kafka®, but not managing it. Confluent's cloud-native, complete, and fully managed service goes above & beyond Kafka, so that your best people can focus on delivering value to your business.
Cloud-Native
We’ve re-engineered Kafka to provide a best-in-class cloud experience, for any scale, without the operational overhead of infrastructure management. Confluent offers the only truly cloud-native experience for Kafka—delivering the serverless, elastic, cost-effective, highly available, and self-serve experience that developers expect.
Complete
Creating and maintaining real-time applications requires more than just open-source software and access to scalable cloud infrastructure. Confluent makes Kafka enterprise-ready and provides customers with the complete set of tools they need to build apps quickly, reliably, and securely. Our fully managed features come ready out of the box, for every use case from proof of concept (POC) to production.
Everywhere
Distributed, complex data architectures can deliver the scale, reliability, and performance to unlock previously unthinkable use cases, but they're incredibly complex to run. Confluent's complete, multi-cloud data streaming platform makes it easy to get data in and out of Kafka with Connect, manage the structure of data using Confluent Schema Registry, and process it in real time using ksqlDB. Confluent meets customers wherever they need to be — powering and uniting real-time data across regions, clouds, and on-premises environments.
지금 바로 체험해 보세요
Confluent는 과거 데이터와 실시간 데이터를 단일 정보 소스에 통합함으로써 완전히 새로운 범주의 현대적인 이벤트 기반 애플리케이션을 쉽게 구축하고 범용 데이터 파이프라인을 확보하며 완벽한 확장성, 보안 및 성능을 갖춘 강력하고 새로운 이용 사례를 활용할 수 있도록 지원합니다.
신규 계정 생성 후 4개월 동안 사용할 수 있는 400달러 상당의 무료 크레딧으로 지금 바로 무료로 체험해 보세요.
Apache Kafka는 시작하기 쉽고 4가지 API(Producer, Consumer, Streams 및 Connect)가 포함된 강력한 이벤트 스트리밍 플랫폼을 제공한다는 이점으로 인해 개발자에게 인기 있는 도구입니다.
개발자는 주로 단일 이용 사례로 시작합니다. 즉, Apache Kafka를 메시지 버퍼로 사용하여 오늘날의 워크로드를 따라갈 수 없는 레거시 데이터베이스를 보호하거나, Connect API를 사용하여 해당 데이터베이스를 함께 제공되는 검색 인덱싱 엔진과 동기화된 상태로 유지함으로써 Streams API를 사용하여 데이터가 도착하는 즉시 처리하여 애플리케이션에 바로 집계를 표시할 수 있습니다.
간단히 말해 Apache Kafka와 해당 API를 사용하면 데이터 기반 앱을 구축하고 복잡한 백엔드 시스템을 간단하게 관리할 수 있습니다. Kafka를 사용하면 데이터가 항상 내결함성이 있고 재생 가능하며 실시간으로 제공된다는 확신으로 안심할 수 있습니다. 실시간 데이터로 앱과 시스템을 처리, 저장 및 연결할 수 있는 단일 이벤트 스트리밍 플랫폼을 제공하여 신속하게 구축할 수 있도록 지원합니다.