Event Stream Processing Market Size, Share & Trends Analysis Report By Component (Software, Service), By Deployment Type (Cloud, On-premise), By Application, By End-use, By Region, And Segment Forecasts, 2023 To 2030

Event stream processing (ESP) is a technique of analyzing, processing, and responding to a continuous stream of data events in real-time. It is a method of capturing, processing, and analyzing data as it flows through a system, without requiring the data to be first stored in a database or data warehouse. Factors such as the increasing demand for real-time analytics and the need for real-time response in critical applications are expected to drive market growth. Moreover, the rise of big data presents a significant growth opportunity for the market.

ESP has various use cases, such as payment processing, predictive maintenance, fraud detection, anomaly detection, and IoT analytics, all of which require immediate action on data. These use cases deal with data points that occur in a continuous stream, each of which is associated with a specific time. ESP is particularly useful in such scenarios as it allows for the identification of patterns and trends that represent crucial insights for users, based on the order and timing of the data points. ESP is also beneficial when data granularity is essential. For instance, in stock trading, the actual changes to a stock price are often more important than the stock price itself. ESP enables tracking all the changes that occur along the way, facilitating better trading decisions.

Stream processing is a valuable tool in the online advertising industry, particularly in social networks that track user behavior, clicks, and interests. This collected data is then used to promote ads to users that they might find interesting. Stream processing is crucial in advertising campaigns as it allows for the real-time processing of user clicks and interests, enabling the display of sponsored content that is relevant to the user.

By component, the market is segmented into solution and service. The solution is further segmented into software and platform. Some of the well-known event stream processing software are IBM Cloud Pak for Integration, Amazon Kinesis Data Streams, Aiven for Apache Kafka, and Red Hat OpenShift Streams for Apache Kafka among others.

In February 2023, Aiven Ltd. introduced a managed edition of Apache Flink that allows for the processing of real-time data streams. Aiven specializes in providing managed and cloud-hosted versions of popular open-source software platforms, released its latest product, Aiven for Apache Flink. This new offering enables businesses to obtain a fully managed version of the Apache Flink platform for real-time data stream processing on Google Cloud, Microsoft Azure, and Amazon Web Services.

Event Stream Processing Market Segmentation

• By Component

  • Solution

    • Software

    • Platform

  • Service

• By Deployment Type

  • Cloud

  • On-premise

• By Application

  • Fraud Detection

  • Process Monitoring

  • Algorithmic Trading

  • Predictive Maintenance

  • Sales and Marketing

• By End Use

  • BFSI

  • IT and Telecom

  • Retail and E-commerce

  • Manufacturing

  • Healthcare

  • Others

• Key players

  • IBM Corporation

  • SAP SE

  • Google LLC

  • Oracle

  • Microsoft

  • TIBCO (Cloud Software Group, Inc.)

  • Hitachi Vantara LLC

  • Amazon Web Services, Inc.

  • Software AG

  • Salesforce, Inc.