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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 — Vorteile und AnwendungsfĂ€lle

Die Vorteile von Streaming-Daten

Die Datenerfassung ist nur ein Teil des Puzzles. Heutzutage haben große Unternehmen einfach nicht die Zeit, Daten als Batch zu verarbeiten. Stattdessen setzen alle – von Betrugserkennungs- und Börsenplattformen ĂŒber Anwendungen fĂŒr Mitfahrgelegenheiten bis hin zu E-Commerce-Websites – auf Echtzeit-Event-Streams.

In Verbindung mit Streaming-Daten können Anwendungen nicht mehr nur Daten integrieren, sondern auch Events verarbeiten, filtern, analysieren und in Echtzeit auf diese reagieren. Dadurch entsteht eine nie dagewesene Vielzahl an AnwendungsfĂ€llen wie Echtzeit-Betrugserkennung, Netflix-Empfehlungen oder ein nahtloses Einkaufserlebnis ĂŒber mehrere GerĂ€te hinweg, das wĂ€hrend des Einkaufens aktualisiert wird.

Kurz gefasst profitieren alle Branchen, die mit großen Mengen an Echtzeitdaten arbeiten, von Plattformen, die ihnen eine kontinuierliche Echtzeit-Event-Stream-Verarbeitung bieten.

Use Cases

Systeme fĂŒr die Datenstromverarbeitung wie Apache Kafka und Confluent erwecken Echtzeit-Daten und -Analysen zum Leben. Obwohl es AnwendungsfĂ€lle fĂŒr Event-Streaming in allen Branchen gibt, bringt die Möglichkeit, Daten in Echtzeit und in großem Maßstab zu integrieren, analysieren, bereinigen und/oder vorherzusagen, auch neue AnwendungsfĂ€lle hervor. Unternehmen können nicht nur historische Daten oder Batch-Daten aus Speichern nutzen, sondern auch wertvolle Einblicke in Data in Motion gewinnen.

Typische AnwendungsfÀlle umfassen:

  • Standortdaten
  • Betrugserkennung
  • Echtzeit-Aktienhandel
  • Marketing-, Vertriebs- und GeschĂ€ftsanalysen
  • Kunden-/BenutzeraktivitĂ€t
  • Überwachung von und Berichte ĂŒber interne IT-Systeme
  • ProtokollĂŒberwachung: Fehlerbehebung bei Systemen, Servern, GerĂ€ten und mehr
  • SIEM (Security Information and Event Management): Analyse von Protokollen und Echtzeit-Ereignisdaten zur Überwachung, Kennzahl-Erstellung und Erkennung von Bedrohungen
  • BestĂ€nde im Einzelhandel/Lager: Bestandsmanagement ĂŒber alle KanĂ€le und Standorte hinweg und nahtloses Benutzererlebnis auf allen GerĂ€ten
  • Zuordnung bei Mitfahrgelegenheiten: Kombination von Standort-, Benutzer- und Preisdaten fĂŒr prĂ€dikative Analysen
  • Zuordnung des Fahrgastes zu den besten Fahrern im Hinblick auf die NĂ€he, den Zielort, Preis und die Wartezeit
  • Maschinelles Lernen und KI: Durch die VerknĂŒpfung von historischen und aktuellen Daten zu einem zentralen Nervensystem entstehen neue AnwendungsfĂ€lle fĂŒr Predictive Analytics

Solange verschiedenste Datentypen verarbeitet, gespeichert oder analysiert werden mĂŒssen, kann Confluent dazu beitragen, die Daten fĂŒr zahlreiche AnwendungsfĂ€lle und in jedem Maßstab nutzbar zu machen.

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.