Event stream processing (ESP) is a technology that can process a continuous flow of data as soon as an event or change happens. By processing single points of data rather than an entire batch, event streaming platforms provide an architecture that enable software to understand, react to, and operate as events occur.
Learn how event stream processing works, its major benefits, and how to get started building event-driven architectures in the free stream processing guide.
Whether in e-commerce, finance, travel, or gaming, every business is inundated with event streams on a day-to-day basis. With customers increasingly looking for responsive interactions and experiences, companies are just discovering the importance of event streaming, allowing real-time data to be processed, stored, and acted upon as real-time events occur. Learn how event streaming is revolutionizing the way business run with an overview of how event streams work, benefits, and use cases.
Similar to streaming data, event sourcing, complex event processing (CEP), event streaming is the continuous flow of data generated with each event, or change of state.
By using event stream processing technologies like Apache Kafka, these events (i.e. a credit card swype, server outage, or social media update) can be processed, stored, analyzed, and acted upon as they're generated in real-time.
In this Online Talk we will cover the basics of event streaming and the Kafka technology that brings event streaming to life. Beyond the basics, we will share some customer use cases on how they are using Kafka to grow their business and stay ahead of the competition.
Data processing is not new. In previous years, legacy infrastructure was more structured because it only had a handful of sources that generated data and the entire system could be architected in a way to specify and unify the data and data structures.
Modern data comes in the form of events.
Pretty much every program uses, and responds to events of some kind: the mouse moving, input becoming available, web forms being submitted, bits of JSON being posted to your endpoint, the sensor on the pear tree detecting that a partridge has landed on it. Kafka encourages you to see the world as sequences of events, which it models as key-value pairs.
Applications that analyze and process data streams need to process one data packet at a time, in sequential order. Each data packet generated will include the source and timestamp to enable applications to work with data streams.
Applications working with data streams will always require two main functions: storage and processing. Storage must be able to record large streams of data in a way that is sequential and consistent. Processing must be able to interact with storage, consume, analyze and run computation on the data.
This also brings up additional challenges and considerations when working with data streams. Many platforms and tools are now available to help companies build streaming data applications.
Legacy batch data processing methods required data to be collected in batch form before it could be processed, stored, or analyzed whereas streaming data flows in continuously, allowing that data to be processed in real time without waiting for it to arrive in batch form.
Today, data arrives naturally as never ending streams of events. This data comes in all volumes, formats, from various locations and cloud, on-premises, or hybrid cloud.
With the complexity of today's modern requirements, legacy data processing methods have become obsolete for most use cases, as it can only process data as groups of transactions collected over time. Modern organizations actively use real-time data streams, acting on up-to-the-millisecond data. This continuous data offers numerous advantages that are transforming the way businesses run.
Some common examples of streaming data are real-time stock trades, retail inventory management, and ride-sharing apps.
For example, when a passenger calls Lyft, not only does the application know which driver to match them to, they know how long it will take based on real-time location data and historical traffic data, and how much it should cost based on both real-time and past data.
Event streams play a key part in the world of big data, providing real-time analyses, data integration, and data ingestion.
Used by 80% of the Fortune 100, Confluent's data streaming platfoirm helps you set your data in motion, no matter where your data resides.
From real-time fraud detection, financial services, and multi-player games, to online social networking, Confluent lets you focus on deriving business value from your data rather than worrying about the underlying mechanics of how data is streamed, integrated, stored, and connected at scale.
데이터 수집은 퍼즐의 한 조각에 불과합니다. 오늘날의 엔터프라이즈 비즈니스는 일괄적으로 처리되는 도구에만 의존할 수 없습니다. 주식 거래 플랫폼, Netflix, 소셜 미디어 피드 등 모든 것이 실시간 데이터 스트림에 의존하고 있습니다.
애플리케이션은 스트리밍 데이터를 활용하면서 데이터를 통합할 뿐만 아니라 이벤트 발생 시 처리, 필터링, 분석하고 이에 대응하는 수준으로 발전했습니다. 이를 통해 전 세계 수백만 명의 사용자, 앱, 시스템으로 확장할 수 있는 예측 분석, 생성형 AI, 원활한 옴니채널 쇼핑 경험과 같은 새로운 사용 사례가 등장하게 되었습니다.
요컨대, 지속적인 실시간 데이터 스트림의 이점을 누릴 수 있는 모든 산업에서 데이터 스트리밍 플랫폼의 이점을 누릴 수 있습니다.
이벤트 스트리밍과 Confluent의 스트림 데이터 플랫폼이 함께 실시간 데이터에 생명을 불어넣습니다. 모든 업계에 이벤트 스트리밍에 대한 사용 사례가 존재하지만 실시간, 대규모로 데이터를 통합, 분석, 문제 해결 및/또는 예측할 수 있는 이 기능은 새로운 사용 사례를 제공합니다. 조직은 스토리지의 과거 데이터 또는 배치 데이터를 이벤트 스트림과 결합하여 발생하는 모든 이벤트에 대해 실행 가능한 인사이트를 얻을 수 있습니다.
일반적인 사용 사례는 다음과 같습니다.
유형에 상관없이 처리, 저장 또는 분석이 필요한 데이터가 존재하는 한, Confluent는 모든 규모의 모든 사용 사례에 대해 데이터를 최대한 활용할 수 있도록 도움을 드릴 수 있습니다.
Built by the original creators of Apache Kafka, Confluent takes Kafka's stream processing technology to a fully managed, multi-cloud data streaming platform. Easily connect 120+ data sources with enterprise grade security, performance, and scalability. Stream data across any infrastructure in minutes.