Kora Engine, Data Quality Rules und mehr in unserem Q2 2023 Launch | Für die Demo registrieren
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.
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.
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.
Data 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.
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 über Anwendungen für 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 Anwendungsfällen wie Echtzeit-Betrugserkennung, Netflix-Empfehlungen oder ein nahtloses Einkaufserlebnis über mehrere Geräte hinweg, das während 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.
Systeme für die Datenstromverarbeitung wie Apache Kafka und Confluent erwecken Echtzeit-Daten und -Analysen zum Leben. Obwohl es Anwendungsfälle für 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 Anwendungsfälle hervor. Unternehmen können nicht nur historische Daten oder Batch-Daten aus Speichern nutzen, sondern auch wertvolle Einblicke in Data in Motion gewinnen.
Typische Anwendungsfälle umfassen:
– Betrugserkennung – Echtzeit-Aktienhandel – Marketing-, Vertriebs- und Geschäftsanalysen – Kunden-/Benutzeraktivität – Überwachung von und Berichte über interne IT-Systeme – Protokollüberwachung: Fehlerbehebung bei Systemen, Servern, Geräten und mehr – SIEM (Security Information and Event Management): Analyse von Protokollen und Echtzeit-Ereignisdaten zur Überwachung, Kennzahl-Erstellung und Erkennung von Bedrohungen – Bestände im Einzelhandel/Lager: Bestandsmanagement über alle Kanäle und Standorte hinweg und nahtloses Benutzererlebnis auf allen Geräten – Zuordnung bei Mitfahrgelegenheiten: Kombination von Standort-, Benutzer- und Preisdaten für prädikative Analysen – Zuordnung des Fahrgastes zu den besten Fahrern im Hinblick auf die Nähe, den Zielort, Preis und die Wartezeit – Maschinelles Lernen und KI: Durch die Verknüpfung von historischen und aktuellen Daten zu einem zentralen Nervensystem entstehen neue Anwendungsfälle für Predictive Analytics
Solange verschiedenste Datentypen verarbeitet, gespeichert oder analysiert werden müssen, kann Confluent dazu beitragen, die Daten für zahlreiche Anwendungsfälle und in jedem Maßstab nutzbar zu machen.
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.