Event Streaming

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.

What is Event Streaming?

What is Event Streaming?

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.

How Streaming Data Works

Data processing is not new. In previous years, legacy infrastructure was much 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 is generated by an infinite amount of sources whether it’s from hardware sensors, servers, mobile devices, applications, web browsers, internal and external and it’s almost impossible to regulate or enforce the data structure or control the volume and frequency of the data generated.

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.

Batch Processing vs Real-Time Streams

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.

Examples of Streaming Data

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.

Data streams play a key part in the world of big data, providing real-time analyses, data integration, and data ingestion.

How Confluent Can Help

How Data Streaming Platforms Can Help

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.

No credit card required! Plus, new signups get a free $400 credit to spend during their first 60 days.

Streaming Benefits & Use Cases


データの収集は氷山の一角に過ぎません。企業にはバッチ形式でのデータの処理を待つだけの余裕はなく、不正行為の検知や株式市場プラットフォーム、ライドシェアアプリや E コマースサイトなど、あらゆる局面でリアルタイムのイベントストリームが活用されています。

ストリーミングデータと組み合わせることで、アプリケーションにデータの統合に加え、イベントを発生次第リアルタイムで処理、フィルタリング、分析、対応できる能力が生まれ、リアルタイムでの不正検知、Netflix でのおすすめ、購入のたびに更新される複数のデバイスでのシームレスなショッピング体験など、新たなユースケースの可能性が広がります。



Apache Kafka や Confluent などのストリーム処理システムは、リアルタイムデータと分析を実現します。イベントストリーミングはあらゆる業界で利用されていますが、データをリアルタイムで大規模に統合、分析、トラブルシューティングや予測できるこうしたシステムの能力で、新たなユースケースが生まれます。組織の過去のデータやストレージ内のバッチデータの利用に加え、躍動するデータからも貴重なインサイトが得られるようになります。

一般的なユースケースの例 :

  • 位置情報データ
  • 不正の検出
  • リアルタイムの株式取引
  • マーケティング、営業、ビジネス分析
  • 顧客/ユーザーのアクティビティ
  • 社内 IT システムの監視とレポーティング
  • ログの監視 : システム、サーバー、デバイスなどのトラブルシューティング
  • SIEM (セキュリティ情報イベント管理): 監視、測定、脅威検知のためのログとリアルタイムイベントデータの分析
  • 小売/倉庫の在庫管理 : すべてのチャネルと場所を対象とした在庫管理とあらゆるデバイスでのシームレスな顧客体験の提供
  • ライドシェアのマッチング : 位置情報、ユーザー、価格設定データを組み合わせて予測分析を実現 - 距離、目的地、価格、待ち時間などから利用者とドライバーをマッチング
  • 機械学習と AI : 過去と現在のデータの組み合わせで一つの中枢神経系を構築し、予測分析で新たな可能性を実現

あらゆる種類の処理、保存、分析すべきデータがある限り、Confluent はあらゆるユースケースと規模でのデータ活用に役立ちます。

Real-world businesses need real-time data.

Why Confluent

Built by the original creators of Apache Kafka, Confluent takes Kafka's stream processing technology to a fully managed, fully automated data streaming platform. Easily connect 120+ data sources with enterprise grade security, reliability, and scalability. Stream data across any cloud, multi-cloud, or serverless infrastructure in minutes.