Market Perspectives

ISG Research is happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The Real-Time Data: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research. As enterprises strive to be data-driven, making a higher proportion of decisions based on data becomes the norm. To differentiate from the pack, enterprises need to process and analyze more data and increase the frequency with which data-driven decisions are made by acting upon data in real time. ISG Research defines real-time data as the sharing, processing and analysis of data communicated in messages and streams of messages generated by enterprise systems, devices and applications as business events occur. Real-time data processing enables enterprises to operate at the speed of business by acting on events as they happen. Despite the overwhelming reliance on batch data processing, it is an artificial construct driven by the historical limitations of computing capabilities to generate and process data at the same time without impacting performance. Real-time data processing has existed for years in industry segments with the most extreme high-performance requirements, such as financial services and telecommunications. In other industries, however, the reliance on batch data processing is so entrenched that processing data in real time has primarily been seen as a niche requirement, separate from the default focus on batch processing of data at rest. Less than one-quarter (22%) of enterprises participating in the ISG Research Analytics and Data Benchmark Research analyze data in real time. Attitudes towards real-time data processing are changing as an increasing number of enterprises recognize that by failing to process and analyze data in real time, there is a risk of failing to operate at the pace of the real world. The pressure on enterprises to improve the ability to process and analyze data in real time is exacerbated by increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations. AI-driven intelligent applications require a new approach to data processing that enables real-time performance of machine learning (ML) on operational data to deliver instant, relevant information for accelerated decision-making. Enterprises differentiate user experiences with real-time, AI-driven functionality. Doing so requires AI models to have access to up-to-date data via streams of events as they are generated in real time, as well as the ability to incorporate model inferencing into streaming analytics pipelines. Enterprises with an over-reliance on batch data processing will not match those able to harness real-time data as it is generated. As demand for real-time interactive applications becomes more pervasive, processing real-time data becomes a more mainstream pursuit. This is aided by the proliferation of products capable of real-time data processing and analytics, which have lowered the cost and technical barriers to developing new applications that take advantage of data in motion. Reliance on batch data processing is so pervasive that for many data practitioners and business executives, real-time data may be an alien concept that appears to have a language of its own. Terms such as messaging, event processing, stream processing and streaming analytics are often used interchangeably, and the nuances are not necessarily clear to the uninitiated. However, the core concepts of real-time data processing are proven and well-defined, and the technologies that implement them are mature and readily available. The starting point for any real-time data strategy is the concept of an event. Simply put, an event is a thing that happens. In the context of an application, an event is a change of state, such as a sensor identifying a new temperature reading. Messaging is the sharing of information between applications, devices and systems about events. As an event occurs, the application, device or system generates a message about the event that is shared with other applications, devices and systems across the enterprise. Direct communication between applications is enabled and managed by application integration, which supports the fulfillment of business processes and workflows that rely on multiple applications operating in concert. While application integration has traditionally relied on point-to-point integration between individual applications, today’s application integration increasingly depends on application programming interfaces and API management. In addition to communicating messages about events between applications, processing a continuous stream of event messages enables enterprises to act on the event data as it is generated and communicated. Stream processing encompasses the ingestion, filtering, integration, transformation, aggregation and enrichment of stream data, and the associated management and governance of stream processing. Stream processing also forms the basis of stream analytics, which uses streaming compute engines to analyze streams of event data using SQL queries and real-time materialized views, including chart-based visualization of streaming data and, more recently, support for machine learning and generative AI model inferencing. Processing and analyzing event data in isolation is valuable. Success with streaming data relies on the holistic management and governance of data in motion and data at rest. In recent years, traditional data processing providers have added support for continually processing streams of event data, while streaming and event specialist providers improve capabilities for the persistence of event data in a data warehouse, data lake or cloud storage for batch processing of historical event data. ISG asserts that by 2027, more than three-quarters of enterprises’ standard information architectures will include streaming data and event processing, allowing enterprises to be more responsive and provide better customer experiences. As more enterprises adopt event-driven architecture, capabilities for the persistence and processing of historical event data increase the potential for streaming and event specialists to stake a claim and be considered an enterprise’s primary data platform provider rather than being utilized for a limited set of use cases. The ability to execute at the speed of business by processing and acting on events as they occur will be the difference between competing and winning with analytics and data. Enterprises evaluating current and future data architecture requirements should consider real-time data technologies alongside more traditional batch-oriented data platforms to provide a holistic view of all data in motion and at rest. The ISG Buyers Guide™ for Real-Time Data evaluates products based on core capabilities such as messaging and event processing, application integration, streaming data and streaming analytics. While products addressing these functional areas are valuable, an enterprise-wide real-time data strategy requires an event-driven architecture that delivers the full breadth of streaming and event functionality. To be included in this Buyers Guide, products must address at least two capabilities: messaging and event processing, application integration, streaming data and streaming analytics. Our assessment also considered whether the functionality was available from a software provider in a single offering or as a suite of products or cloud services. This research evaluates the following software providers that offer products that address key elements of real-time data as we define it: Actian, Aiven, Alibaba Cloud, Altair, AWS, Cloud Software Group, Cloudera, Confluent, Cumulocity, Databricks, Google Cloud, GridGain, Hazelcast, Huawei Cloud, IBM, Informatica, Materialize, Microsoft, Oracle, Qubole, Redpanda, SAS, Solace, Striim and Tencent Cloud. This research-based index evaluates the full business and information technology value of real-time data software offerings. We encourage you to learn more about our Buyers Guide and its effectiveness as a provider selection and RFI/RFP tool. We urge organizations to do a thorough job of evaluating real-time data offerings in this Buyers Guide as both the results of our in-depth analysis of these software providers and as an evaluation methodology. The Buyers Guide can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP and the definition of an RFI. The Buyers Guide for Real-Time Data in 2025 finds AWS first on the list, followed by Google Cloud and Microsoft. Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: AWS. Google Cloud. Microsoft. The Leaders in Customer Experience are: Databricks. Informatica. Oracle. The Leaders across any of the seven categories are: Oracle, which has achieved this rating in six of the seven categories. Informatica in five categories. Databricks and Google Cloud in three categories. Microsoft in two categories. AWS and Solace in one category. The overall performance chart provides a visual representation of how providers rate across product and customer experience. Software providers with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, providers that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as providers of “merit.” Software providers that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close provider scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every enterprise or process. Although there is a high degree of commonality in how organizations handle real-time data, there are many idiosyncrasies and differences that can make one provider’s offering a better fit than another. ISG Research has made every effort to encompass in this Buyers Guide the overall product and customer experience from our real-time data blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which software provider and products best fit an enterprise’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects. You can find more details on our community as well as on our expertise in the research for this Buyers Guide.

ISG Buyers Guide for Real-Time Data in 2025 Classifies and Rates Software Providers

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