Transparency Market Research
Distributed stream computing platforms (DCSPs) refers to a model called simple scalable streaming system (S4) platform. It is capable of solving real-world problems with reference to search applications that rely on data mining and complex machine learning algorithms. Current commercial search engines, such as Bing, Google, Ask.com and Yahoo, usually provide organic web results in response to user queries and then provide with textual advertisements. In order to give the most relevant ads in an optimal position on the page, highly advanced algorithms are developed that dynamically estimate the probability of a click on the ad given the context. A major search engine may process thousands of queries per second, which may include several ads per page. In order to process user feedback, a low latency, scalable stream processing engine S4 was developed.
With the emergence of internet of things (IoT) and big data analytics, the distributed stream computing platforms (DCSPs) market is flourishing. The distributed stream processing systems (DCPS) form an essential component of any IoT stack, therefore widespread adoption of IoT technology is driving market growth. Also, big data technology is becoming popular in handling data stream, which leads to development of many distributed stream computing systems. With the quantity of data growing and the speed of data increasing, big data computing has become an emerging trend for future. Stream computing is an efficient way to manage big data by providing very low-latency velocities with the help of parallel processing architectures. Thus, it is becoming the fastest and most proficient way to obtain useful knowledge from big data, allowing organizations to react quickly when problems appear. However, diversity of workloads and complexity of stream computing are among major factors restraining market growth. DCSPs are general-purpose, scalable, distributed, pluggable, partially fault-tolerant platforms that allow programmers to easily develop applications for processing boundless streams of data continuously. Thus, distributed stream computing platforms (DCSPs) market is expected to witness significant growth during the forecast period due to highly flexible design of such models.
The global distributed stream computing platforms (DCSPs) market is categorized on the basis of deployment model, end use and geographic regions. Segmentation on the basis of deployment model includes cloud based and on-premise. Distributed stream computing platforms (DCSPs) market by cloud based model is further categorized into public cloud, private cloud and hybrid cloud. Segmentation on the basis of end user industry comprises of retail, communication, banking, financial services and insurance (BFSI), healthcare, public sector, defense & security, life sciences and others. The distributed stream computing platforms (DCSPs) market has been studied for five geographic regions namely North America, Europe, Asia Pacific, Middle East and Africa, and South America.
Among major end-use verticals, BFSI sector is expected to lead the global distributed stream computing platforms (DCSPs) market during the forecast period because of increasing demand for real time data processing. Cloud based deployment model is forecasted to hold most of the market share due to increasing adoption of cloud based technologies and platform as a service model. Emergence of big data technology and advanced analytics likely to foster the demand for distributed stream computing platforms (DCSPs). North America is predicted to be major revenue shareholder due to the presence of major market players, technological innovations and relatively mature market for big data stream computing. Europe is expected to be the second major revenue holder due to early adoption of cloud based technology while Asia Pacific region is expected to experience fastest growth rate.
The key players of the global distributed stream computing platforms (DCSPs) market are Cisco Systems, Inc., data Artisans, IBM Corporation, Oracle Corporation, SAS Institute Inc., SAP SE, TIBCO Software Inc., Informatica, Impetus Technologies, EsperTech Inc., and DataTorrent among others.
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