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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

Avantages des données diffusées en continu

La collecte de données n'est qu'une pièce du puzzle. De nos jours, les entreprises ne peuvent plus se permettre d'attendre que les données soient traitées par lots. Au contraire, tout le monde s'appuie sur les flux d'événements en temps réel, des systèmes de détection des fraudes aux sites Web d'e-commerce en passant par les plateformes de trading et les applications VTC.

Associ√©es √† des donn√©es diffus√©es en continu, les applications √©voluent de sorte √† int√©grer les donn√©es et √† les traiter, les filtrer, les analyser et r√©agir aux √©v√©nements en temps r√©el, d√®s qu'ils se produisent. Cela ouvre tout un nouveau champ de possibles en termes de cas d'utilisation, notamment du c√īt√© de la d√©tection de fraude, des recommandations Netflix ou des exp√©riences de shopping homog√®nes d'un appareil √† l'autre, qui se mettent √† jour au fur et √† mesure de vos achats.

En bref, tout secteur qui traite de grands volumes de données en temps réel peut bénéficier de plateformes de traitement des flux d'événements continus en temps réel.

Cas d'utilisation

Les systèmes de traitement des flux comme Apache Kafka et Confluent donnent vie aux données et analyses en temps réel. S'il existe des cas d'utilisation liés à la diffusion d'événements dans chaque secteur, cette capacité à intégrer, analyser, dépanner et/ou prévoir les données en temps réel à grande échelle ouvre de nouvelles opportunités. Les entreprises peuvent non seulement utiliser les données passées ou les données stockées par lots, mais aussi obtenir des informations précieuses concernant les données en mouvement.

Voici des exemples de cas d'utilisation typiques :

  • Donn√©es de localisation
  • D√©tection de fraude
  • Trading d'actions en temps r√©el
  • Marketing, commerce et analyses d'entreprise
  • Activit√© des clients/utilisateurs
  • Surveillance des syst√®mes informatiques internes et √©laboration de rapports
  • Suivi des journaux¬†: d√©pannage des syst√®mes, serveurs, appareils, etc.
  • SIEM (gestion des √©v√©nements et des informations de s√©curit√©)¬†: analyse des fichiers journaux et des donn√©es li√©es aux √©v√©nements en temps r√©el √† des fins de suivi, d'identification d'indicateurs et de d√©tection des menaces
  • Gestion des stocks de la vente au d√©tail¬†/¬†des entrep√īts¬†: gestion des stocks √† travers tous les canaux et emplacements, et offre d'une exp√©rience utilisateur fluide d'un appareil √† l'autre
  • Mise en correspondance de VTC¬†: allier les donn√©es concernant la localisation, l'utilisateur et le tarif √† des fins d'analyse pr√©dictive - Associer les usagers aux meilleurs chauffeurs en termes de proximit√©, destination, tarif et d√©lai d'attente
  • Apprentissage automatique et IA¬†: en combinant les donn√©es pass√©es et pr√©sentes pour obtenir un syst√®me nerveux central, l'analyse pr√©dictive offre de nouvelles perspectives

Tant qu'il y a encore un type de données à traiter, stocker ou analyser, Confluent peut vous aider à tirer parti de vos données pour n'importe quel cas d'utilisation, à n'importe quelle échelle.

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.