Event Stream Processing Market Size, Share, Opportunities, And Trends By Component (Solution (Software, Platform), Service), By Deployment (Cloud, On-premise), By Application (Fraud Detection, Process Monitoring, Algorithmic Trading, Predictive Maintenance, Sales And Marketing), By End User (BFSI, IT And Telecom, Retail And E-commerce, Manufacturing, Healthcare, Others), And By Geography - Forecasts From 2023 To 2028

  • Published : Oct 2023
  • Report Code : KSI061616650
  • Pages : 147

The event stream processing market is projected to show steady growth during the forecast period.

Processing data in real-time as it flows through a data stream source is known as event stream processing. This technique involves filtering, analyzing, and processing data as it comes through the pipeline. The applications of event stream processing range from real-time analytics to fraud detection and IoT data processing. The process of event stream processing is reactive and changes the traditional analytics procedure by processing events as they occur. This results in a faster reaction time and enables proactive measures to be taken before a situation escalates. The ability to respond in real-time is an advantage of event stream processing and it is used across various industries where stream data is generated from people, sensors, or machines. As IoT technology continues to expand, event stream processing will see an increase in real-world applications. Big data often involves streaming data, also known as event stream processing. This data is generated continuously by numerous data sources, such as sensors or server logs. Streaming data processing software analyzes the data incrementally and performs real-time aggregation and correlation, filtering, or sampling. The stream is often stored to contribute to the historical record.


The industry of event stream processing is rapidly growing and is utilized in almost every industry where stream data is generated, whether it's from people, sensors, or machines. As IoT technology continues to advance, the real-world applications of stream processing will continue to increase. The market is fuelled by the demand for real-time analytics, fraud detection, and IoT data processing. Event stream processing is employed for various purposes, including real-time analytics, fraud detection, and IoT data processing. The market consists of three distinct terms: event, stream, and processing. An event is a data point in the system that continuously generates data, while the stream refers to the continuous delivery of events from that data source. The market comprises two primary classes of technologies: the system that stores the events and the technology that assists developers in writing applications that act on the events. The former component pertains to data storage and stores data based on a timestamp, while the latter component pertains to the technology that helps developers write applications that take action on the events. The market is particularly useful when data granularity is crucial, such as in the actual changes to a stock price, which are often more important to a trader than the stock price itself. By analyzing stream data in real-time, unusual events, significant deviations from normal values, and developing trends can be detected, which can then inform real-time responses.


  • Real-time analytics: In today's fast-paced business world, immediate access to data and insights is crucial for making informed decisions. For this reason, event stream processing has become an increasingly popular method for analyzing data in real time. This technology is especially important for industries like finance, where real-time analytics can provide traders with up-to-the-minute information on stock prices and trends. With the ability to process large amounts of data quickly and accurately, event stream processing is a powerful tool for businesses looking to stay ahead of the curve and make data-driven decisions.
  • Fraud detection: The practice of event stream processing has become increasingly popular in recent years due to its ability to detect fraudulent activities in real time. This technology is particularly important for businesses, especially those in the banking industry, where quick action is crucial to prevent financial losses. By analyzing data in real-time, event stream processing can identify patterns and anomalies that may indicate fraudulent behavior, allowing companies to take immediate action to prevent further damage. With the help of this innovative technology, businesses can safeguard their operations and protect their customers from potential harm.
  • IoT data processing: Event stream processing plays a crucial role in effectively handling data generated by IoT devices, especially for businesses that heavily rely on instant insights to drive their decision-making process. This technological driver holds particular significance in industries such as manufacturing, where real-time data processing offers immense potential to optimize production processes. By harnessing event stream processing, companies in the manufacturing sector can dynamically analyze incoming data from IoT devices and promptly respond to any anomalies or emerging patterns. This capability enables them to mitigate potential risks, enhance operational efficiency, and streamline overall production procedures. Moreover, event stream processing empowers businesses to gain real-time visibility into critical aspects of their operations, facilitating the implementation of predictive maintenance strategies and the identification of potential bottlenecks in production.
  • Data granularity: Event stream processing is a highly valuable tool, particularly when data granularity is of utmost importance. Traders, for example, often find themselves more concerned with the actual changes in stock prices rather than the stock price itself. By analyzing stream data in real time, event stream processing enables the detection of unusual events, significant deviations from normal values, and the identification of developing trends. This invaluable information, obtained in real time, empowers traders to make informed decisions and respond promptly to market shifts. The ability to detect and analyze these minute details allows for a more accurate and efficient understanding of the financial landscape, ultimately leading to better outcomes in the market. By incorporating event stream processing into their data analysis strategies, traders can gain a competitive edge by staying ahead of the curve and capitalizing on emerging opportunities.
  • Real-time response time: Processing data in real-time offers numerous advantages. One major benefit is the opportunity it provides for a real-time response time. By analyzing and acting on data as it is generated, organizations can achieve faster reaction times, facilitating quicker decision-making and problem-solving. For example, in the context of customer service, real-time data processing allows businesses to identify and resolve issues proactively, minimizing customer frustration and improving satisfaction. Moreover, the ability to process data in real-time enables organizations to take proactive measures before a situation is over. This means that potential problems can be detected and addressed promptly, preventing them from escalating into more significant issues. Whether it is monitoring financial transactions for fraudulent activity or overseeing the performance of complex systems, real-time data processing empowers organizations to stay one step ahead and take strategic actions promptly.

Products offered by key companies:

  • IBM Event Streams is a fully managed event streaming service that enables applications and services to communicate in real time. It is built on Apache Kafka, a popular open-source event streaming platform. IBM Event Streams provides several features that make it easy to deploy and manage event streaming applications.
  • TIBCO Event Streams is an event stream processing platform that enables organizations to process and analyze data in real time. It is a highly scalable and reliable platform that can handle high volumes of data from a variety of sources, including IoT devices, sensors, and applications.

Prominent growth in the cloud segment within the event stream processing market:

The cloud segment within the event stream processing market has seen prominent growth in recent years. Cloud computing offers several benefits to businesses across various industries. One key advantage is scalability, which is especially crucial for industries that require rapid scaling. Take the finance sector, for example. Traders need the ability to scale their operations quickly in response to actual changes in stock prices. By harnessing cloud computing, they can make better and more informed decisions, ultimately leading to more successful outcomes. Additionally, cloud computing is highly cost-effective, making it a valuable solution for businesses that prioritize efficiency and affordability. In manufacturing, for instance, cost-effective cloud solutions can optimize production processes and streamline operations. Another advantage of cloud computing is the democratized access to computational power and infrastructure. This is particularly important for businesses that rely on automated capabilities that would be too costly to develop on-premise. An excellent example lies in the demand for cloud-based deployment in video streaming processes. As most enterprises lack the necessary networks and infrastructure to handle heavy online traffic, the cloud has become a vital component in delivering smooth and uninterrupted streaming experiences.

The Asia Pacific region is expected to hold a significant share of the event stream processing market:

The Asia Pacific region is expected to hold a significant share of the event stream processing market. The Asia Pacific region has experienced a remarkable surge in the utilization of IoT devices in recent years, resulting in the generation of massive amounts of data that necessitate real-time processing. This growing need for immediate data crunching has led to a corresponding increase in demand for event stream processing solutions within the region. One major contributing factor is the rising requirement for real-time analytics, which plays a vital role for businesses that depend on prompt insights to make well-informed decisions. The financial industry, for instance, greatly benefits from real-time analytics by enabling traders to make more astute choices based on actual fluctuations in stock prices. Furthermore, the Asia Pacific is home to some of the world's most rapidly expanding economies, which has fueled the demand for these state-of-the-art processing solutions. The growth is also driven by a rising preference for cost-effective and scalable cloud-based options. Additionally, there has been a significant increase in investment in technology across the region, leading to the development and emergence of new event stream processing applications, thereby expanding the market. Lastly, given its substantial population size, the Asia Pacific generates vast amounts of data, creating an essential need for real-time processing solutions capable of handling such an immense volume.

Key developments:

  • In October 2022, Microsoft released the Azure Stream Analytics no-code editor, a drag-and-drop interface for building stream processing jobs. It is generally available and hosted by Azure Event Hubs, Microsoft's streaming data platform. Azure Stream Analytics is a managed real-time analytics service. Its no-code editor allows users to develop a Stream Analytics job without writing any code.
  • In August 2022, DataStax, a company that specializes in real-time data, and Decodable, a company that specializes in streaming processing platforms, announced a new partnership to help developers build modern real-time applications and deliver data services in minutes at a significantly lower cost.


  • By Component
    • Solution
      • Software
      • Platform
    • Service
  • By Deployment
    • Cloud
    • On-premise
  • By Application
    • Fraud Detection
    • Process Monitoring
    • Algorithmic Trading
    • Predictive Maintenance
    • Sales and Marketing
  • By End User
    • BFSI
    • IT and Telecom
    • Retail and E-commerce
    • Manufacturing
    • Healthcare
    • Others
  • By Geography
    • North America
      • United States
      • Canada
      • Mexico
    • South America
      • Brazil
      • Argentina
      • Others
    • Europe
      • United Kingdom
      • Germany
      • France
      • Spain
      • Others
    • Middle East and Africa
      • Saudi Arabia
      • UAE
      • Israel
      • Others
    • Asia Pacific
      • Japan
      • China
      • India
      • South Korea
      • Indonesia
      • Thailand
      • Others


1.1. Market Overview

1.2. Market Definition

1.3. Scope of the Study

1.4. Market Segmentation

1.5. Currency

1.6. Assumptions

1.7. Base, and Forecast Years Timeline


2.1. Research Data

2.2. Assumptions


3.1. Research Highlights


4.1. Market Drivers

4.2. Market Restraints

4.3. Porter’s Five Force Analysis

4.3.1. Bargaining Power of Suppliers

4.3.2. Bargaining Power of Buyers

4.3.3. Threat of New Entrants

4.3.4. Threat of Substitutes

4.3.5. Competitive Rivalry in the Industry

4.4. Industry Value Chain Analysis


5.1. Introduction

5.2. Solution

5.2.1. Software

5.2.2. Platform

5.3. Service


6.1. Introduction

6.2. Cloud

6.3. On-premise


7.1. Introduction

7.2. Fraud Detection

7.3. Process Monitoring

7.4. Algorithmic Trading

7.5. Predictive Maintenance

7.6. Sales and Marketing


8.1. Introduction

8.2. BFSI

8.3. IT and Telecom

8.4. Retail and E-commerce

8.5. Manufacturing

8.6. Healthcare

8.7. Others


9.1. Introduction

9.2. North America

9.2.1. United States

9.2.2. Canada

9.2.3. Mexico

9.3. South America

9.3.1. Brazil

9.3.2. Argentina

9.3.3. Others

9.4. Europe

9.4.1. United Kingdom

9.4.2. Germany

9.4.3. France

9.4.4. Spain

9.4.5. Others

9.5. The Middle East and Africa

9.5.1. Saudi Arabia

9.5.2. UAE

9.5.3. Israel

9.5.4. Others

9.6. Asia Pacific

9.6.1. Japan

9.6.2. China

9.6.3. India

9.6.4. South Korea

9.6.5. Indonesia

9.6.6. Thailand

9.6.7. Others


10.1. Major Players and Strategy Analysis

10.2. Market Share Analysis

10.3. Mergers, Acquisitions, Agreements, and Collaborations


11.1.  IBM Corporation

11.2. SAP SE

11.3. Google LLC

11.4. Oracle

11.5. Microsoft

11.6. TIBCO (Cloud Software Group, Inc.)

11.7. Amazon Web Services, Inc.

11.8. Software AG

11.9. Salesforce, Inc

IBM Corporation


Google LLC



TIBCO (Cloud Software Group, Inc.)

Amazon Web Services, Inc.

Software AG

Salesforce, Inc