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レポート概要
| 本調査資料は、世界の複合イベント処理市場について調査し、イントロダクション、調査手法、エグゼクティブサマリー、市場動向、種類別（ソフトウェア、サービス）分析、企業種類別（中小企業、大企業）分析、産業別（金融、マネージドモビリティ、政府・防衛、小売、医療、その他）分析、地域別分析、競争状況、投資分析、市場機会/将来の見通しなどを徹底分析したものです。 ・イントロダクション ・調査手法 ・エグゼクティブサマリー ・市場動向 ・世界の複合イベント処理市場規模：種類別（ソフトウェア、サービス） ・世界の複合イベント処理市場規模：企業種類別（中小企業、大企業） ・世界の複合イベント処理市場規模：産業別（金融、マネージドモビリティ、政府・防衛、小売、医療、その他） ・世界の複合イベント処理市場規模：地域別 ・競争状況 ・投資分析 ・市場機会/将来の見通し |
The Complex Event Processing Market has registered a CAGR of 28.74% over the forecast period 2021 – 2026. With the growing use of sensors and connecting devices, the amount of data getting stored is increasing tremendously. In the traditional DBMS method, the problem of analyzing this data at a real-time basis is a challenge. In response, Complex Event Processing (CEP) addresses this problem, as it works on the stored query rather than stored data.
– The advantages of CEP approach are distinct, given that CEP queries are applied on a potentially infinite stream of data. Moreover, inputs are processed immediately. Once the system processes all events for a matching sequence, results are shown directly. This aspect effectively leads to CEP’s real-time analytics capability.
– With the revolution of the internet, the need for real-time data analytics growing rapidly from the past few years. Companies are making high investments for industrial automation, which is rising the developments in machine learning. Additionally, varied industries, along with Big Data, are making the web more complicated, which is ultimately driving the complex event processor market.
– Along with the need for real-time data analytics, the amount of data getting stored is reaching high regularly. Hence, the demand for efficient CEP systems to effective real-time data processing is growing.
Key Market Trends
BFSI End-user Segment to Grow Significantly
– With globally increasing IT spending toward enterprise software and communication services, which is estimated to be close to 50% of the global IT spending in 2018, CEP solutions gained a lot of attention, especially from BFSI sector for real-time analysis and gathering business intelligence for better decision making.
– Credit card companies use CEP with Big Data analytics to manage fraud better. When a pattern of fraud incidence emerges, the company can block the credit card quickly before the company can experience significant losses, as it deals with the moving flow of data. The underlying system is expected to correlate the incoming transactions, track the stream of event data, and trigger a process.
– Banks and trading companies are highly investing in blockchain technology, which is giving rise to the use of CEP systems. CEP systems help in integrating the lifecycle of digital transactions among various business entities, customers, systems, and technologies. With CEP, event handlers need to be configured to listen for changes in the blockchain or the connected endpoints and then correlate and invoke appropriate CEP rules to either derive an action or alert.
– BFSI sector contributes 40% of the TCS revenue, the rising adoption of technologies like blockchain and Big Data analytics in the sector is giving rise to the application of CEP systems to formulate decision making at a real-time basis.
Asia-Pacific to Witness Significant Growth
– Asia-Pacific is one of the fastest developing regions in blockchain technology, contributed to over 40% development in the world. The digitalization initiatives by the developing countries, like India, are driving the digital payments and banks are deploying the blockchain technology. Hence, fuels the demand for CEP solution for its efficient integration.
– Growth in the Asia-Pacific is mainly driven by government initiatives and large multi-million dollar technology deals in the banking vertical. Some of the others factors, such as increasing demand for data storage in small to medium enterprises (SMEs) and increasing proliferation of smartphones, laptops, and tablets in the Asia-Pacific region have augmented the demand of machine learning applications in the region.
– Retail and consumer goods companies in the region see the applicability of machine learning (ML) to drive improvements in customer service and operational efficiency. For instance, the Azure cloud is helping retail and consumer brands to improve the shopping experience by ensuring that shelves are stocked and the products are always available when, where, and how the consumer wants to shop.
Competitive Landscape
The market is consolidated as a significant share of the market lies, with the major market players. Innovation in the market requires the developers to have a better understanding of the industrial process to deliver a suitable solution. Innovation also drives close collaboration among the stakeholders during development and customization to suit the end users’ need.
– February 2019 – Google announced its intention to acquire Alooma, which allows enterprises to combine all of their data sources into services, like Google’s BigQuery, Amazon’s Redshift, Snowflake, and Azure. The company has promised that with Alooma it will handle the data pipelines and manage them for its users.
– January 2019 – IBM is in the process of acquiring the mainframe solution provider T-Systems. This acquisition is expected to help the company in gaining a stronger hold in mainframe offerings.
Reasons to Purchase this report:
– The market estimate (ME) sheet in Excel format
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1 INTRODUCTION
1.1 Study Deliverables
1.2 Study Assumptions
1.3 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Technology Snapshot
4.3 Market Drivers
4.3.1 Development in the Field of Machine Learning and Data Analytics
4.4 Market Restraints
4.4.1 Lack of Consistency in Results
4.5 Industry Value Chain Analysis
4.6 Industry Attractiveness – Porter’s Five Forces Analysis
4.6.1 Threat of New Entrants
4.6.2 Bargaining Power of Buyers/Consumers
4.6.3 Bargaining Power of Suppliers
4.6.4 Threat of Substitute Products
4.6.5 Intensity of Competitive Rivalry
5 MARKET SEGMENTATION
5.1 By Type
5.1.1 Software
5.1.2 Services
5.2 By Enterprise Type
5.2.1 Small and Medium Enterprise
5.2.2 Large Enterprise
5.3 By End-user Vertical
5.3.1 BFSI
5.3.2 Managed Mobility
5.3.3 Government and Defense
5.3.4 Retail
5.3.5 Healthcare
5.3.6 Telecom and IT Industry
5.3.7 Media and Entertainment
5.3.8 Manufacturing
5.3.9 Other End-user Verticals
5.4 Geography
5.4.1 North America
5.4.2 Europe
5.4.3 Asia-Pacific
5.4.4 Latin America
5.4.5 Middle East & Africa
6 COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 IBM Corporation
6.1.2 SAP SE
6.1.3 Oracle Corporation
6.1.4 Tibco Software Inc.
6.1.5 SAS Institute Inc.
6.1.6 Informatica Corporation
6.1.7 Nastel Technologies Inc.
6.1.8 Software AG
6.1.9 Espertech Inc.
6.1.10 Cisco Systems Inc.
6.1.11 Red Lambda Inc.
7 INVESTMENT ANALYSIS
8 MARKET OPPORTUNITIES AND FUTURE TRENDS